Impact of Interstock and Rootstock on the Growth and Productivity of Mango (Mangifera indica L.) in the San Lorenzo Valley, Peru

1 Project Setup

library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
library(factoextra)
library(corrplot)
library(magrittr)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd(), "\n")
Project:  C:/INIA/GIT/prochira_injertos 
session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.3 (2025-02-28 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Peru.utf8
 ctype    Spanish_Peru.utf8
 tz       America/Lima
 date     2025-04-10
 pandoc   3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
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 huito         * 0.2.5   2024-09-05 [1] CRAN (R 4.4.3)
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 [1] C:/Users/INIA/AppData/Local/Programs/R/R-4.4.3/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

2 Import data

Data were imported from the field book evaluated during the 2017-2019 growing seasons. The evaluations focused on the agronomic traits and fruit biometrics of the mango crop.

url <- "https://docs.google.com/spreadsheets/d/1E_cUmiRFoPLGAj7AQC6HZBD6hXtqzg2UHPvtWr2QR_U/edit#gid=2112492836"

gs <- url %>% 
  as_sheets_id()

ley <- gs %>% 
  range_read("leyenda") %>% 
  rename(tratamientos = TRATAM) %>% 
  rename_with(~ tolower(.))

rdt <- gs %>% 
  range_read("db") %>% 
  merge(., ley) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , height = alt_planta
         , n_fruits = n_frutos
         , flowering = per_floracion
         , sproud = per_brote
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(across(year:n_plant, ~ as.factor(.))) 

glimpse(rdt)
## Rows: 648
## Columns: 13
## $ year      <fct> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
## $ n         <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
## $ treat     <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 231, 231, 231, …
## $ n_treat   <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, …
## $ stock     <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ edge      <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ scion     <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, …
## $ block     <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ n_plant   <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, …
## $ height    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ n_fruits  <dbl> 170, 200, 310, NA, 235, 185, 180, 132, 80, 231, 198, 195, 20…
## $ flowering <dbl> 60, 50, 50, NA, 80, 60, 40, 90, 90, 60, 70, 40, 90, 80, 80, …
## $ sproud    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
  
fru <-  gs %>% 
  range_read("db_frutos") %>% 
  merge(., ley, ) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , weigth = peso
         , long = largo
         , n_fruits = n_frutos
         , diameter_1 = diametro_1
         , diameter_2 = diametro_2
         , sample = muestra
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(diameter_average = (diameter_1 + diameter_2)/2) %>% 
  mutate(across(year:sample, ~ as.factor(.)))

glimpse(fru)
## Rows: 240
## Columns: 16
## $ year             <fct> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023,…
## $ n                <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
## $ treat            <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 231…
## $ n_treat          <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ stock            <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ edge             <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ scion            <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT,…
## $ block            <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ n_plant          <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2,…
## $ sample           <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3,…
## $ n_fruits         <dbl> 26, 26, 26, 26, 26, 35, 35, 35, 35, 35, 118, 118, 118…
## $ weigth           <dbl> 270, 520, 385, 230, 285, 600, 457, 520, 665, 305, 422…
## $ long             <dbl> 86, 111, 103, 86, 88, 116, 101, 115, 126, 91, 100, 10…
## $ diameter_1       <dbl> 78, 90, 86, 70, 78, 96, 88, 88, 103, 78, 91, 84, 84, …
## $ diameter_2       <dbl> 74, 83, 75, 69, 71, 87, 88, 81, 95, 80, 83, 76, 72, 8…
## $ diameter_average <dbl> 76.0, 86.5, 80.5, 69.5, 74.5, 91.5, 88.0, 84.5, 99.0,…
ley %>% kable(caption = "Interstock grafting treatments", align = 'c')
Interstock grafting treatments
n_trat tratamientos patron puente yema
1 141 CHULUCANAS CHULUCANAS KENT
2 231 CHATO JULIE KENT
3 241 CHULUCANAS CHATO KENT
4 211 CHATO IRWIN KENT
5 131 CHULUCANAS JULIE KENT
6 111 CHULUCANAS IRWIN KENT
7 221 CHATO CHATO KENT
8 121 CHATO CHULUCANAS KENT

rdt %>% kable(caption = "Evaluation of the agronomic characteristics of mango", align = 'c')
Evaluation of the agronomic characteristics of mango
year n treat n_treat stock edge scion block n_plant height n_fruits flowering sproud
2017 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 170 60
2017 2 141 1 CHULUCANAS CHULUCANAS KENT 1 2 200 50
2017 3 141 1 CHULUCANAS CHULUCANAS KENT 1 3 310 50
2017 4 141 1 CHULUCANAS CHULUCANAS KENT 1 4
2017 5 141 1 CHULUCANAS CHULUCANAS KENT 1 5 235 80
2017 6 141 1 CHULUCANAS CHULUCANAS KENT 1 6 185 60
2017 7 141 1 CHULUCANAS CHULUCANAS KENT 1 7 180 40
2017 8 141 1 CHULUCANAS CHULUCANAS KENT 1 8 132 90
2017 9 141 1 CHULUCANAS CHULUCANAS KENT 1 9 80 90
2017 10 231 2 CHATO JULIE KENT 1 1 231 60
2017 11 231 2 CHATO JULIE KENT 1 2 198 70
2017 12 231 2 CHATO JULIE KENT 1 3 195 40
2017 13 231 2 CHATO JULIE KENT 1 4 200 90
2017 14 231 2 CHATO JULIE KENT 1 5 180 80
2017 15 231 2 CHATO JULIE KENT 1 6 140 80
2017 16 231 2 CHATO JULIE KENT 1 7 200 60
2017 17 231 2 CHATO JULIE KENT 1 8 186 75
2017 18 231 2 CHATO JULIE KENT 1 9
2017 19 121 8 CHATO CHULUCANAS KENT 1 1
2017 20 121 8 CHATO CHULUCANAS KENT 1 2 165 75
2017 21 121 8 CHATO CHULUCANAS KENT 1 3 210 75
2017 22 121 8 CHATO CHULUCANAS KENT 1 4 201 80
2017 23 121 8 CHATO CHULUCANAS KENT 1 5 110 80
2017 24 121 8 CHATO CHULUCANAS KENT 1 6 215 90
2017 25 121 8 CHATO CHULUCANAS KENT 1 7 200 80
2017 26 121 8 CHATO CHULUCANAS KENT 1 8 160 80
2017 27 121 8 CHATO CHULUCANAS KENT 1 9 162 75
2017 28 211 4 CHATO IRWIN KENT 1 1 204 70
2017 29 211 4 CHATO IRWIN KENT 1 2 124 80
2017 30 211 4 CHATO IRWIN KENT 1 3 130 80
2017 31 211 4 CHATO IRWIN KENT 1 4 6 5
2017 32 211 4 CHATO IRWIN KENT 1 5 90 35
2017 33 211 4 CHATO IRWIN KENT 1 6 170 60
2017 34 211 4 CHATO IRWIN KENT 1 7 165 45
2017 35 211 4 CHATO IRWIN KENT 1 8 100 25
2017 36 211 4 CHATO IRWIN KENT 1 9 70 25
2017 37 131 5 CHULUCANAS JULIE KENT 1 1 75 80
2017 38 131 5 CHULUCANAS JULIE KENT 1 2 200 80
2017 39 131 5 CHULUCANAS JULIE KENT 1 3 184 80
2017 40 131 5 CHULUCANAS JULIE KENT 1 4 102 70
2017 41 131 5 CHULUCANAS JULIE KENT 1 5 190 60
2017 42 131 5 CHULUCANAS JULIE KENT 1 6 180 70
2017 43 131 5 CHULUCANAS JULIE KENT 1 7 175 75
2017 44 131 5 CHULUCANAS JULIE KENT 1 8 120 80
2017 45 131 5 CHULUCANAS JULIE KENT 1 9 155 60
2017 46 241 3 CHULUCANAS CHATO KENT 1 1 230 90
2017 47 241 3 CHULUCANAS CHATO KENT 1 2 110 90
2017 48 241 3 CHULUCANAS CHATO KENT 1 3 185 90
2017 49 241 3 CHULUCANAS CHATO KENT 1 4 220 90
2017 50 241 3 CHULUCANAS CHATO KENT 1 5 180 90
2017 51 241 3 CHULUCANAS CHATO KENT 1 6 175 60
2017 52 241 3 CHULUCANAS CHATO KENT 1 7 210 90
2017 53 241 3 CHULUCANAS CHATO KENT 1 8 180 90
2017 54 241 3 CHULUCANAS CHATO KENT 1 9 178 90
2017 55 111 6 CHULUCANAS IRWIN KENT 1 1 150 90
2017 56 111 6 CHULUCANAS IRWIN KENT 1 2 160 85
2017 57 111 6 CHULUCANAS IRWIN KENT 1 3 200 90
2017 58 111 6 CHULUCANAS IRWIN KENT 1 4 140 90
2017 59 111 6 CHULUCANAS IRWIN KENT 1 5 175 90
2017 60 111 6 CHULUCANAS IRWIN KENT 1 6 200 80
2017 61 111 6 CHULUCANAS IRWIN KENT 1 7 208 90
2017 62 111 6 CHULUCANAS IRWIN KENT 1 8 300 90
2017 63 111 6 CHULUCANAS IRWIN KENT 1 9 230 90
2017 64 221 7 CHATO CHATO KENT 1 1 190 90
2017 65 221 7 CHATO CHATO KENT 1 2 206 90
2017 66 221 7 CHATO CHATO KENT 1 3 145 80
2017 67 221 7 CHATO CHATO KENT 1 4 51 20
2017 68 221 7 CHATO CHATO KENT 1 5 45 30
2017 69 221 7 CHATO CHATO KENT 1 6 163 80
2017 70 221 7 CHATO CHATO KENT 1 7 35 15
2017 71 221 7 CHATO CHATO KENT 1 8 200 80
2017 72 221 7 CHATO CHATO KENT 1 9 32 15
2017 73 211 4 CHATO IRWIN KENT 2 1 170 90
2017 74 211 4 CHATO IRWIN KENT 2 2 350 80
2017 75 211 4 CHATO IRWIN KENT 2 3 200 90
2017 76 211 4 CHATO IRWIN KENT 2 4 154 70
2017 77 211 4 CHATO IRWIN KENT 2 5 180 90
2017 78 211 4 CHATO IRWIN KENT 2 6 130 90
2017 79 211 4 CHATO IRWIN KENT 2 7 145 90
2017 80 211 4 CHATO IRWIN KENT 2 8 130 90
2017 81 211 4 CHATO IRWIN KENT 2 9 220 90
2017 82 121 8 CHATO CHULUCANAS KENT 2 1 223 90
2017 83 121 8 CHATO CHULUCANAS KENT 2 2 220 90
2017 84 121 8 CHATO CHULUCANAS KENT 2 3 230 90
2017 85 121 8 CHATO CHULUCANAS KENT 2 4 200 90
2017 86 121 8 CHATO CHULUCANAS KENT 2 5 210 90
2017 87 121 8 CHATO CHULUCANAS KENT 2 6 150 90
2017 88 121 8 CHATO CHULUCANAS KENT 2 7 220 80
2017 89 121 8 CHATO CHULUCANAS KENT 2 8 270 90
2017 90 121 8 CHATO CHULUCANAS KENT 2 9 170 85
2017 91 231 2 CHATO JULIE KENT 2 1 28 90
2017 92 231 2 CHATO JULIE KENT 2 2 250 90
2017 93 231 2 CHATO JULIE KENT 2 3 115 70
2017 94 231 2 CHATO JULIE KENT 2 4 160 80
2017 95 231 2 CHATO JULIE KENT 2 5 200 90
2017 96 231 2 CHATO JULIE KENT 2 6 150 90
2017 97 231 2 CHATO JULIE KENT 2 7 120 90
2017 98 231 2 CHATO JULIE KENT 2 8 230 90
2017 99 231 2 CHATO JULIE KENT 2 9 240 80
2017 100 141 1 CHULUCANAS CHULUCANAS KENT 2 1 210 80
2017 101 141 1 CHULUCANAS CHULUCANAS KENT 2 2 205 90
2017 102 141 1 CHULUCANAS CHULUCANAS KENT 2 3 210 90
2017 103 141 1 CHULUCANAS CHULUCANAS KENT 2 4 170 80
2017 104 141 1 CHULUCANAS CHULUCANAS KENT 2 5 120 80
2017 105 141 1 CHULUCANAS CHULUCANAS KENT 2 6 240 90
2017 106 141 1 CHULUCANAS CHULUCANAS KENT 2 7 280 90
2017 107 141 1 CHULUCANAS CHULUCANAS KENT 2 8 260 90
2017 108 141 1 CHULUCANAS CHULUCANAS KENT 2 9 170 70
2017 109 221 7 CHATO CHATO KENT 2 1 142 90
2017 110 221 7 CHATO CHATO KENT 2 2 120 90
2017 111 221 7 CHATO CHATO KENT 2 3 140 90
2017 112 221 7 CHATO CHATO KENT 2 4 180 90
2017 113 221 7 CHATO CHATO KENT 2 5 160 90
2017 114 221 7 CHATO CHATO KENT 2 6 130 90
2017 115 221 7 CHATO CHATO KENT 2 7 70 90
2017 116 221 7 CHATO CHATO KENT 2 8 115 90
2017 117 221 7 CHATO CHATO KENT 2 9 104 90
2017 118 111 6 CHULUCANAS IRWIN KENT 2 1
2017 119 111 6 CHULUCANAS IRWIN KENT 2 2 208 90
2017 120 111 6 CHULUCANAS IRWIN KENT 2 3 110 90
2017 121 111 6 CHULUCANAS IRWIN KENT 2 4 140 90
2017 122 111 6 CHULUCANAS IRWIN KENT 2 5 108 80
2017 123 111 6 CHULUCANAS IRWIN KENT 2 6 120 90
2017 124 111 6 CHULUCANAS IRWIN KENT 2 7 250 90
2017 125 111 6 CHULUCANAS IRWIN KENT 2 8 220 90
2017 126 111 6 CHULUCANAS IRWIN KENT 2 9 235 80
2017 127 241 3 CHULUCANAS CHATO KENT 2 1 170 80
2017 128 241 3 CHULUCANAS CHATO KENT 2 2 182 80
2017 129 241 3 CHULUCANAS CHATO KENT 2 3 148 90
2017 130 241 3 CHULUCANAS CHATO KENT 2 4 160 90
2017 131 241 3 CHULUCANAS CHATO KENT 2 5 23 25
2017 132 241 3 CHULUCANAS CHATO KENT 2 6 175 80
2017 133 241 3 CHULUCANAS CHATO KENT 2 7 227 90
2017 134 241 3 CHULUCANAS CHATO KENT 2 8 180 90
2017 135 241 3 CHULUCANAS CHATO KENT 2 9 50 90
2017 136 131 5 CHULUCANAS JULIE KENT 2 1 180 80
2017 137 131 5 CHULUCANAS JULIE KENT 2 2 240 90
2017 138 131 5 CHULUCANAS JULIE KENT 2 3 124 70
2017 139 131 5 CHULUCANAS JULIE KENT 2 4 220 85
2017 140 131 5 CHULUCANAS JULIE KENT 2 5 240 80
2017 141 131 5 CHULUCANAS JULIE KENT 2 6 90 80
2017 142 131 5 CHULUCANAS JULIE KENT 2 7 220 85
2017 143 131 5 CHULUCANAS JULIE KENT 2 8 250 90
2017 144 131 5 CHULUCANAS JULIE KENT 2 9 174 90
2017 145 121 8 CHATO CHULUCANAS KENT 3 1 170 85
2017 146 121 8 CHATO CHULUCANAS KENT 3 2 198 80
2017 147 121 8 CHATO CHULUCANAS KENT 3 3 125 90
2017 148 121 8 CHATO CHULUCANAS KENT 3 4 124 90
2017 149 121 8 CHATO CHULUCANAS KENT 3 5 120 90
2017 150 121 8 CHATO CHULUCANAS KENT 3 6 135 80
2017 151 121 8 CHATO CHULUCANAS KENT 3 7 150 90
2017 152 121 8 CHATO CHULUCANAS KENT 3 8 330 90
2017 153 121 8 CHATO CHULUCANAS KENT 3 9 201 80
2017 154 211 4 CHATO IRWIN KENT 3 1 210 90
2017 155 211 4 CHATO IRWIN KENT 3 2 100 90
2017 156 211 4 CHATO IRWIN KENT 3 3 160 90
2017 157 211 4 CHATO IRWIN KENT 3 4 260 90
2017 158 211 4 CHATO IRWIN KENT 3 5
2017 159 211 4 CHATO IRWIN KENT 3 6 160 85
2017 160 211 4 CHATO IRWIN KENT 3 7 210 80
2017 161 211 4 CHATO IRWIN KENT 3 8 200 85
2017 162 211 4 CHATO IRWIN KENT 3 9 225 90
2017 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 300 90
2017 164 141 1 CHULUCANAS CHULUCANAS KENT 3 2 195 70
2017 165 141 1 CHULUCANAS CHULUCANAS KENT 3 3 210 80
2017 166 141 1 CHULUCANAS CHULUCANAS KENT 3 4 230 80
2017 167 141 1 CHULUCANAS CHULUCANAS KENT 3 5 205 90
2017 168 141 1 CHULUCANAS CHULUCANAS KENT 3 6 204 80
2017 169 141 1 CHULUCANAS CHULUCANAS KENT 3 7 90 90
2017 170 141 1 CHULUCANAS CHULUCANAS KENT 3 8 180 70
2017 171 141 1 CHULUCANAS CHULUCANAS KENT 3 9 37 20
2017 172 231 2 CHATO JULIE KENT 3 1 305 90
2017 173 231 2 CHATO JULIE KENT 3 2 120 90
2017 174 231 2 CHATO JULIE KENT 3 3 160 80
2017 175 231 2 CHATO JULIE KENT 3 4 82 90
2017 176 231 2 CHATO JULIE KENT 3 5 110 90
2017 177 231 2 CHATO JULIE KENT 3 6 140 80
2017 178 231 2 CHATO JULIE KENT 3 7 208 85
2017 179 231 2 CHATO JULIE KENT 3 8 240 90
2017 180 231 2 CHATO JULIE KENT 3 9 160 90
2017 181 111 6 CHULUCANAS IRWIN KENT 3 1 60 40
2017 182 111 6 CHULUCANAS IRWIN KENT 3 2 113 50
2017 183 111 6 CHULUCANAS IRWIN KENT 3 3 210 80
2017 184 111 6 CHULUCANAS IRWIN KENT 3 4 260 90
2017 185 111 6 CHULUCANAS IRWIN KENT 3 5 150 80
2017 186 111 6 CHULUCANAS IRWIN KENT 3 6 80 50
2017 187 111 6 CHULUCANAS IRWIN KENT 3 7 180 90
2017 188 111 6 CHULUCANAS IRWIN KENT 3 8 196 80
2017 189 111 6 CHULUCANAS IRWIN KENT 3 9 220 80
2017 190 221 7 CHATO CHATO KENT 3 1 230 90
2017 191 221 7 CHATO CHATO KENT 3 2 210 90
2017 192 221 7 CHATO CHATO KENT 3 3 150 85
2017 193 221 7 CHATO CHATO KENT 3 4 315 90
2017 194 221 7 CHATO CHATO KENT 3 5 220 90
2017 195 221 7 CHATO CHATO KENT 3 6
2017 196 221 7 CHATO CHATO KENT 3 7 250 90
2017 197 221 7 CHATO CHATO KENT 3 8 160 80
2017 198 221 7 CHATO CHATO KENT 3 9 230 90
2017 199 131 5 CHULUCANAS JULIE KENT 3 1
2017 200 131 5 CHULUCANAS JULIE KENT 3 2 280 90
2017 201 131 5 CHULUCANAS JULIE KENT 3 3 103 90
2017 202 131 5 CHULUCANAS JULIE KENT 3 4 180 90
2017 203 131 5 CHULUCANAS JULIE KENT 3 5 250 90
2017 204 131 5 CHULUCANAS JULIE KENT 3 6
2017 205 131 5 CHULUCANAS JULIE KENT 3 7 190 90
2017 206 131 5 CHULUCANAS JULIE KENT 3 8 140 90
2017 207 131 5 CHULUCANAS JULIE KENT 3 9 210 80
2017 208 241 3 CHULUCANAS CHATO KENT 3 1 190 80
2017 209 241 3 CHULUCANAS CHATO KENT 3 2 220 90
2017 210 241 3 CHULUCANAS CHATO KENT 3 3 140 80
2017 211 241 3 CHULUCANAS CHATO KENT 3 4 210 75
2017 212 241 3 CHULUCANAS CHATO KENT 3 5 200 90
2017 213 241 3 CHULUCANAS CHATO KENT 3 6 290 90
2017 214 241 3 CHULUCANAS CHATO KENT 3 7 140 50
2017 215 241 3 CHULUCANAS CHATO KENT 3 8 170 90
2017 216 241 3 CHULUCANAS CHATO KENT 3 9 160 85
2018 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 95
2018 2 141 1 CHULUCANAS CHULUCANAS KENT 1 2 95
2018 3 141 1 CHULUCANAS CHULUCANAS KENT 1 3 315
2018 4 141 1 CHULUCANAS CHULUCANAS KENT 1 4
2018 5 141 1 CHULUCANAS CHULUCANAS KENT 1 5 230
2018 6 141 1 CHULUCANAS CHULUCANAS KENT 1 6 232
2018 7 141 1 CHULUCANAS CHULUCANAS KENT 1 7 80
2018 8 141 1 CHULUCANAS CHULUCANAS KENT 1 8 90
2018 9 141 1 CHULUCANAS CHULUCANAS KENT 1 9 20
2018 10 231 2 CHATO JULIE KENT 1 1 80
2018 11 231 2 CHATO JULIE KENT 1 2 50
2018 12 231 2 CHATO JULIE KENT 1 3 92
2018 13 231 2 CHATO JULIE KENT 1 4 163
2018 14 231 2 CHATO JULIE KENT 1 5 5
2018 15 231 2 CHATO JULIE KENT 1 6 145
2018 16 231 2 CHATO JULIE KENT 1 7 80
2018 17 231 2 CHATO JULIE KENT 1 8 40
2018 18 231 2 CHATO JULIE KENT 1 9
2018 19 121 8 CHATO CHULUCANAS KENT 1 1
2018 20 121 8 CHATO CHULUCANAS KENT 1 2 90
2018 21 121 8 CHATO CHULUCANAS KENT 1 3 90
2018 22 121 8 CHATO CHULUCANAS KENT 1 4 295
2018 23 121 8 CHATO CHULUCANAS KENT 1 5 382
2018 24 121 8 CHATO CHULUCANAS KENT 1 6 270
2018 25 121 8 CHATO CHULUCANAS KENT 1 7 90
2018 26 121 8 CHATO CHULUCANAS KENT 1 8 90
2018 27 121 8 CHATO CHULUCANAS KENT 1 9 50
2018 28 211 4 CHATO IRWIN KENT 1 1 50
2018 29 211 4 CHATO IRWIN KENT 1 2 60
2018 30 211 4 CHATO IRWIN KENT 1 3 242
2018 31 211 4 CHATO IRWIN KENT 1 4 70
2018 32 211 4 CHATO IRWIN KENT 1 5 320
2018 33 211 4 CHATO IRWIN KENT 1 6 300
2018 34 211 4 CHATO IRWIN KENT 1 7 80
2018 35 211 4 CHATO IRWIN KENT 1 8 90
2018 36 211 4 CHATO IRWIN KENT 1 9 95
2018 37 131 5 CHULUCANAS JULIE KENT 1 1 25
2018 38 131 5 CHULUCANAS JULIE KENT 1 2 250
2018 39 131 5 CHULUCANAS JULIE KENT 1 3 80
2018 40 131 5 CHULUCANAS JULIE KENT 1 4 240
2018 41 131 5 CHULUCANAS JULIE KENT 1 5 200
2018 42 131 5 CHULUCANAS JULIE KENT 1 6 10
2018 43 131 5 CHULUCANAS JULIE KENT 1 7 75
2018 44 131 5 CHULUCANAS JULIE KENT 1 8 60
2018 45 131 5 CHULUCANAS JULIE KENT 1 9 90
2018 46 241 3 CHULUCANAS CHATO KENT 1 1 20
2018 47 241 3 CHULUCANAS CHATO KENT 1 2 40
2018 48 241 3 CHULUCANAS CHATO KENT 1 3 70
2018 49 241 3 CHULUCANAS CHATO KENT 1 4 250
2018 50 241 3 CHULUCANAS CHATO KENT 1 5 195
2018 51 241 3 CHULUCANAS CHATO KENT 1 6 280
2018 52 241 3 CHULUCANAS CHATO KENT 1 7 30
2018 53 241 3 CHULUCANAS CHATO KENT 1 8 80
2018 54 241 3 CHULUCANAS CHATO KENT 1 9 90
2018 55 111 6 CHULUCANAS IRWIN KENT 1 1 80
2018 56 111 6 CHULUCANAS IRWIN KENT 1 2 146
2018 57 111 6 CHULUCANAS IRWIN KENT 1 3 15
2018 58 111 6 CHULUCANAS IRWIN KENT 1 4 138
2018 59 111 6 CHULUCANAS IRWIN KENT 1 5 90
2018 60 111 6 CHULUCANAS IRWIN KENT 1 6 190
2018 61 111 6 CHULUCANAS IRWIN KENT 1 7
2018 62 111 6 CHULUCANAS IRWIN KENT 1 8 80
2018 63 111 6 CHULUCANAS IRWIN KENT 1 9 95
2018 64 221 7 CHATO CHATO KENT 1 1 180
2018 65 221 7 CHATO CHATO KENT 1 2 50
2018 66 221 7 CHATO CHATO KENT 1 3 80
2018 67 221 7 CHATO CHATO KENT 1 4 80
2018 68 221 7 CHATO CHATO KENT 1 5 90
2018 69 221 7 CHATO CHATO KENT 1 6 178
2018 70 221 7 CHATO CHATO KENT 1 7
2018 71 221 7 CHATO CHATO KENT 1 8 204
2018 72 221 7 CHATO CHATO KENT 1 9 97
2018 73 211 4 CHATO IRWIN KENT 2 1
2018 74 211 4 CHATO IRWIN KENT 2 2 266 90
2018 75 211 4 CHATO IRWIN KENT 2 3 187 85
2018 76 211 4 CHATO IRWIN KENT 2 4 5
2018 77 211 4 CHATO IRWIN KENT 2 5 220 90
2018 78 211 4 CHATO IRWIN KENT 2 6 35
2018 79 211 4 CHATO IRWIN KENT 2 7 90
2018 80 211 4 CHATO IRWIN KENT 2 8 95
2018 81 211 4 CHATO IRWIN KENT 2 9 90
2018 82 121 8 CHATO CHULUCANAS KENT 2 1 85
2018 83 121 8 CHATO CHULUCANAS KENT 2 2 108 90
2018 84 121 8 CHATO CHULUCANAS KENT 2 3 200 90
2018 85 121 8 CHATO CHULUCANAS KENT 2 4 95
2018 86 121 8 CHATO CHULUCANAS KENT 2 5 95
2018 87 121 8 CHATO CHULUCANAS KENT 2 6 90
2018 88 121 8 CHATO CHULUCANAS KENT 2 7 280 85
2018 89 121 8 CHATO CHULUCANAS KENT 2 8 90
2018 90 121 8 CHATO CHULUCANAS KENT 2 9 90
2018 91 231 2 CHATO JULIE KENT 2 1
2018 92 231 2 CHATO JULIE KENT 2 2 300 85
2018 93 231 2 CHATO JULIE KENT 2 3 290 80
2018 94 231 2 CHATO JULIE KENT 2 4 50
2018 95 231 2 CHATO JULIE KENT 2 5 214 90
2018 96 231 2 CHATO JULIE KENT 2 6 90
2018 97 231 2 CHATO JULIE KENT 2 7 30
2018 98 231 2 CHATO JULIE KENT 2 8 85
2018 99 231 2 CHATO JULIE KENT 2 9 85
2018 100 141 1 CHULUCANAS CHULUCANAS KENT 2 1 305 85
2018 101 141 1 CHULUCANAS CHULUCANAS KENT 2 2 85
2018 102 141 1 CHULUCANAS CHULUCANAS KENT 2 3 75
2018 103 141 1 CHULUCANAS CHULUCANAS KENT 2 4 80
2018 104 141 1 CHULUCANAS CHULUCANAS KENT 2 5 230 75
2018 105 141 1 CHULUCANAS CHULUCANAS KENT 2 6 150 95
2018 106 141 1 CHULUCANAS CHULUCANAS KENT 2 7 80
2018 107 141 1 CHULUCANAS CHULUCANAS KENT 2 8 95
2018 108 141 1 CHULUCANAS CHULUCANAS KENT 2 9 80
2018 109 221 7 CHATO CHATO KENT 2 1 60
2018 110 221 7 CHATO CHATO KENT 2 2 80
2018 111 221 7 CHATO CHATO KENT 2 3 5
2018 112 221 7 CHATO CHATO KENT 2 4 3
2018 113 221 7 CHATO CHATO KENT 2 5 150 70
2018 114 221 7 CHATO CHATO KENT 2 6 140 30
2018 115 221 7 CHATO CHATO KENT 2 7 0
2018 116 221 7 CHATO CHATO KENT 2 8 150 10
2018 117 221 7 CHATO CHATO KENT 2 9 80
2018 118 111 6 CHULUCANAS IRWIN KENT 2 1
2018 119 111 6 CHULUCANAS IRWIN KENT 2 2 95
2018 120 111 6 CHULUCANAS IRWIN KENT 2 3 90
2018 121 111 6 CHULUCANAS IRWIN KENT 2 4 85
2018 122 111 6 CHULUCANAS IRWIN KENT 2 5 10
2018 123 111 6 CHULUCANAS IRWIN KENT 2 6 5
2018 124 111 6 CHULUCANAS IRWIN KENT 2 7 160 70
2018 125 111 6 CHULUCANAS IRWIN KENT 2 8 325 90
2018 126 111 6 CHULUCANAS IRWIN KENT 2 9 265 95
2018 127 241 3 CHULUCANAS CHATO KENT 2 1 80
2018 128 241 3 CHULUCANAS CHATO KENT 2 2 20
2018 129 241 3 CHULUCANAS CHATO KENT 2 3 65 20
2018 130 241 3 CHULUCANAS CHATO KENT 2 4 61 20
2018 131 241 3 CHULUCANAS CHATO KENT 2 5
2018 132 241 3 CHULUCANAS CHATO KENT 2 6 255 95
2018 133 241 3 CHULUCANAS CHATO KENT 2 7 40
2018 134 241 3 CHULUCANAS CHATO KENT 2 8 60
2018 135 241 3 CHULUCANAS CHATO KENT 2 9 50
2018 136 131 5 CHULUCANAS JULIE KENT 2 1 20
2018 137 131 5 CHULUCANAS JULIE KENT 2 2 95
2018 138 131 5 CHULUCANAS JULIE KENT 2 3 124 90
2018 139 131 5 CHULUCANAS JULIE KENT 2 4 220 90
2018 140 131 5 CHULUCANAS JULIE KENT 2 5 260 95
2018 141 131 5 CHULUCANAS JULIE KENT 2 6
2018 142 131 5 CHULUCANAS JULIE KENT 2 7 90
2018 143 131 5 CHULUCANAS JULIE KENT 2 8 95
2018 144 131 5 CHULUCANAS JULIE KENT 2 9 95
2018 145 121 8 CHATO CHULUCANAS KENT 3 1 95
2018 146 121 8 CHATO CHULUCANAS KENT 3 2 95
2018 147 121 8 CHATO CHULUCANAS KENT 3 3 95
2018 148 121 8 CHATO CHULUCANAS KENT 3 4 110 90
2018 149 121 8 CHATO CHULUCANAS KENT 3 5 147 95
2018 150 121 8 CHATO CHULUCANAS KENT 3 6 100
2018 151 121 8 CHATO CHULUCANAS KENT 3 7 75
2018 152 121 8 CHATO CHULUCANAS KENT 3 8 390 90
2018 153 121 8 CHATO CHULUCANAS KENT 3 9 95
2018 154 211 4 CHATO IRWIN KENT 3 1 80
2018 155 211 4 CHATO IRWIN KENT 3 2 95
2018 156 211 4 CHATO IRWIN KENT 3 3 40
2018 157 211 4 CHATO IRWIN KENT 3 4 160 65
2018 158 211 4 CHATO IRWIN KENT 3 5
2018 159 211 4 CHATO IRWIN KENT 3 6 126 75
2018 160 211 4 CHATO IRWIN KENT 3 7 20
2018 161 211 4 CHATO IRWIN KENT 3 8 136 25
2018 162 211 4 CHATO IRWIN KENT 3 9 40
2018 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 390 80
2018 164 141 1 CHULUCANAS CHULUCANAS KENT 3 2 95
2018 165 141 1 CHULUCANAS CHULUCANAS KENT 3 3 90
2018 166 141 1 CHULUCANAS CHULUCANAS KENT 3 4 70
2018 167 141 1 CHULUCANAS CHULUCANAS KENT 3 5 250 95
2018 168 141 1 CHULUCANAS CHULUCANAS KENT 3 6 95
2018 169 141 1 CHULUCANAS CHULUCANAS KENT 3 7 0
2018 170 141 1 CHULUCANAS CHULUCANAS KENT 3 8 272 90
2018 171 141 1 CHULUCANAS CHULUCANAS KENT 3 9 95
2018 172 231 2 CHATO JULIE KENT 3 1 203 50
2018 173 231 2 CHATO JULIE KENT 3 2 0
2018 174 231 2 CHATO JULIE KENT 3 3 0
2018 175 231 2 CHATO JULIE KENT 3 4 5
2018 176 231 2 CHATO JULIE KENT 3 5 2
2018 177 231 2 CHATO JULIE KENT 3 6 33 15
2018 178 231 2 CHATO JULIE KENT 3 7 116 40
2018 179 231 2 CHATO JULIE KENT 3 8 60
2018 180 231 2 CHATO JULIE KENT 3 9 50
2018 181 111 6 CHULUCANAS IRWIN KENT 3 1 90
2018 182 111 6 CHULUCANAS IRWIN KENT 3 2 95
2018 183 111 6 CHULUCANAS IRWIN KENT 3 3 245 90
2018 184 111 6 CHULUCANAS IRWIN KENT 3 4 290 90
2018 185 111 6 CHULUCANAS IRWIN KENT 3 5 70
2018 186 111 6 CHULUCANAS IRWIN KENT 3 6 85
2018 187 111 6 CHULUCANAS IRWIN KENT 3 7 90
2018 188 111 6 CHULUCANAS IRWIN KENT 3 8 80
2018 189 111 6 CHULUCANAS IRWIN KENT 3 9 160 50
2018 190 221 7 CHATO CHATO KENT 3 1 220 80
2018 191 221 7 CHATO CHATO KENT 3 2 10
2018 192 221 7 CHATO CHATO KENT 3 3 0
2018 193 221 7 CHATO CHATO KENT 3 4 182 50
2018 194 221 7 CHATO CHATO KENT 3 5 0
2018 195 221 7 CHATO CHATO KENT 3 6
2018 196 221 7 CHATO CHATO KENT 3 7 180 80
2018 197 221 7 CHATO CHATO KENT 3 8 5
2018 198 221 7 CHATO CHATO KENT 3 9 2
2018 199 131 5 CHULUCANAS JULIE KENT 3 1
2018 200 131 5 CHULUCANAS JULIE KENT 3 2 85
2018 201 131 5 CHULUCANAS JULIE KENT 3 3 70
2018 202 131 5 CHULUCANAS JULIE KENT 3 4 213 70
2018 203 131 5 CHULUCANAS JULIE KENT 3 5 300 90
2018 204 131 5 CHULUCANAS JULIE KENT 3 6
2018 205 131 5 CHULUCANAS JULIE KENT 3 7 10
2018 206 131 5 CHULUCANAS JULIE KENT 3 8 20
2018 207 131 5 CHULUCANAS JULIE KENT 3 9 200 60
2018 208 241 3 CHULUCANAS CHATO KENT 3 1 80
2018 209 241 3 CHULUCANAS CHATO KENT 3 2 25
2018 210 241 3 CHULUCANAS CHATO KENT 3 3 60
2018 211 241 3 CHULUCANAS CHATO KENT 3 4 210 60
2018 212 241 3 CHULUCANAS CHATO KENT 3 5 75 15
2018 213 241 3 CHULUCANAS CHATO KENT 3 6 188 40
2018 214 241 3 CHULUCANAS CHATO KENT 3 7 10
2018 215 241 3 CHULUCANAS CHATO KENT 3 8 80
2018 216 241 3 CHULUCANAS CHATO KENT 3 9 40
2019 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 3.15 180 95 80
2019 2 141 1 CHULUCANAS CHULUCANAS KENT 1 2 3.60 280 90 75
2019 3 141 1 CHULUCANAS CHULUCANAS KENT 1 3 3.65 234 80 70
2019 4 141 1 CHULUCANAS CHULUCANAS KENT 1 4
2019 5 141 1 CHULUCANAS CHULUCANAS KENT 1 5 3.00 110 90 40
2019 6 141 1 CHULUCANAS CHULUCANAS KENT 1 6 3.40 202 95 80
2019 7 141 1 CHULUCANAS CHULUCANAS KENT 1 7 3.40 202 80 75
2019 8 141 1 CHULUCANAS CHULUCANAS KENT 1 8 3.20 108 70 50
2019 9 141 1 CHULUCANAS CHULUCANAS KENT 1 9 2.70 80 90 0
2019 10 231 2 CHATO JULIE KENT 1 1 3.65 245 85 5
2019 11 231 2 CHATO JULIE KENT 1 2 3.95 228 80 25
2019 12 231 2 CHATO JULIE KENT 1 3 4.85 320 95 20
2019 13 231 2 CHATO JULIE KENT 1 4 4.35 307 90 40
2019 14 231 2 CHATO JULIE KENT 1 5 4.00 251 80 75
2019 15 231 2 CHATO JULIE KENT 1 6 4.20 264 70 35
2019 16 231 2 CHATO JULIE KENT 1 7 4.25 280 95 65
2019 17 231 2 CHATO JULIE KENT 1 8 4.80 288 95 80
2019 18 231 2 CHATO JULIE KENT 1 9 70
2019 19 121 8 CHATO CHULUCANAS KENT 1 1 4.05 70
2019 20 121 8 CHATO CHULUCANAS KENT 1 2 4.00 122 90 80
2019 21 121 8 CHATO CHULUCANAS KENT 1 3 3.70 118 90 80
2019 22 121 8 CHATO CHULUCANAS KENT 1 4 4.25 152 90 75
2019 23 121 8 CHATO CHULUCANAS KENT 1 5 4.35 333 90 40
2019 24 121 8 CHATO CHULUCANAS KENT 1 6 3.70 221 95 75
2019 25 121 8 CHATO CHULUCANAS KENT 1 7 4.50 304 85 90
2019 26 121 8 CHATO CHULUCANAS KENT 1 8 4.15 182 80 90
2019 27 121 8 CHATO CHULUCANAS KENT 1 9 3.95 210 95 70
2019 28 211 4 CHATO IRWIN KENT 1 1 4.20 395 90 30
2019 29 211 4 CHATO IRWIN KENT 1 2 3.95 160 90 75
2019 30 211 4 CHATO IRWIN KENT 1 3 4.15 226 90 70
2019 31 211 4 CHATO IRWIN KENT 1 4 4.45 194 85 70
2019 32 211 4 CHATO IRWIN KENT 1 5 4.10 315 75 60
2019 33 211 4 CHATO IRWIN KENT 1 6 4.45 130 65 30
2019 34 211 4 CHATO IRWIN KENT 1 7 4.40 175 80 30
2019 35 211 4 CHATO IRWIN KENT 1 8 4.00 140 80 60
2019 36 211 4 CHATO IRWIN KENT 1 9 3.80 97 65 60
2019 37 131 5 CHULUCANAS JULIE KENT 1 1 3.00 125 90 80
2019 38 131 5 CHULUCANAS JULIE KENT 1 2 3.95 185 70 65
2019 39 131 5 CHULUCANAS JULIE KENT 1 3 4.10 203 80 90
2019 40 131 5 CHULUCANAS JULIE KENT 1 4 3.65 160 90 45
2019 41 131 5 CHULUCANAS JULIE KENT 1 5 3.65 137 90 60
2019 42 131 5 CHULUCANAS JULIE KENT 1 6 3.10 138 95 65
2019 43 131 5 CHULUCANAS JULIE KENT 1 7 3.70 184 95 80
2019 44 131 5 CHULUCANAS JULIE KENT 1 8 3.60 144 90 80
2019 45 131 5 CHULUCANAS JULIE KENT 1 9 3.80 210 85 75
2019 46 241 3 CHULUCANAS CHATO KENT 1 1 4.45 219 90 80
2019 47 241 3 CHULUCANAS CHATO KENT 1 2 4.65 240 90 70
2019 48 241 3 CHULUCANAS CHATO KENT 1 3 4.25 265 95 85
2019 49 241 3 CHULUCANAS CHATO KENT 1 4 4.35 215 95 75
2019 50 241 3 CHULUCANAS CHATO KENT 1 5 4.45 361 90 75
2019 51 241 3 CHULUCANAS CHATO KENT 1 6 4.05 144 80 75
2019 52 241 3 CHULUCANAS CHATO KENT 1 7 4.35 215 85 70
2019 53 241 3 CHULUCANAS CHATO KENT 1 8 4.10 104 90 45
2019 54 241 3 CHULUCANAS CHATO KENT 1 9 4.05 97 70 75
2019 55 111 6 CHULUCANAS IRWIN KENT 1 1 4.30 76 60 40
2019 56 111 6 CHULUCANAS IRWIN KENT 1 2 3.65 220 90 65
2019 57 111 6 CHULUCANAS IRWIN KENT 1 3 4.10 245 80 60
2019 58 111 6 CHULUCANAS IRWIN KENT 1 4 3.70 220 95 70
2019 59 111 6 CHULUCANAS IRWIN KENT 1 5 3.40 98 80 30
2019 60 111 6 CHULUCANAS IRWIN KENT 1 6 3.85 172 95 80
2019 61 111 6 CHULUCANAS IRWIN KENT 1 7
2019 62 111 6 CHULUCANAS IRWIN KENT 1 8 3.45 148 80 80
2019 63 111 6 CHULUCANAS IRWIN KENT 1 9
2019 64 221 7 CHATO CHATO KENT 1 1 4.45 206 95 20
2019 65 221 7 CHATO CHATO KENT 1 2 3.65 260 95 50
2019 66 221 7 CHATO CHATO KENT 1 3 3.40 132 95 70
2019 67 221 7 CHATO CHATO KENT 1 4 3.15 25 70
2019 68 221 7 CHATO CHATO KENT 1 5 3.35 51 80 70
2019 69 221 7 CHATO CHATO KENT 1 6 4.25 210 95 70
2019 70 221 7 CHATO CHATO KENT 1 7 3.30 65 80 80
2019 71 221 7 CHATO CHATO KENT 1 8 3.85 217 80 70
2019 72 221 7 CHATO CHATO KENT 1 9 3.00 65 85 70
2019 73 211 4 CHATO IRWIN KENT 2 1 4.85 23 30 20
2019 74 211 4 CHATO IRWIN KENT 2 2 4.05 164 90 70
2019 75 211 4 CHATO IRWIN KENT 2 3 3.95 187 70 70
2019 76 211 4 CHATO IRWIN KENT 2 4 3.10 34 70 40
2019 77 211 4 CHATO IRWIN KENT 2 5 3.05 167 80 65
2019 78 211 4 CHATO IRWIN KENT 2 6 3.40 130 90 45
2019 79 211 4 CHATO IRWIN KENT 2 7 4.05 312 60 60
2019 80 211 4 CHATO IRWIN KENT 2 8 3.75 136 60 40
2019 81 211 4 CHATO IRWIN KENT 2 9 3.70 139 85 65
2019 82 121 8 CHATO CHULUCANAS KENT 2 1 3.45 185 85 70
2019 83 121 8 CHATO CHULUCANAS KENT 2 2 3.75 58 90 50
2019 84 121 8 CHATO CHULUCANAS KENT 2 3 3.70 118 90 65
2019 85 121 8 CHATO CHULUCANAS KENT 2 4 3.75 152 70 60
2019 86 121 8 CHATO CHULUCANAS KENT 2 5 3.65 75 90
2019 87 121 8 CHATO CHULUCANAS KENT 2 6 4.05 187 90 70
2019 88 121 8 CHATO CHULUCANAS KENT 2 7 3.95 120 80 80
2019 89 121 8 CHATO CHULUCANAS KENT 2 8 3.40 150 90 40
2019 90 121 8 CHATO CHULUCANAS KENT 2 9 2.90 87 70 70
2019 91 231 2 CHATO JULIE KENT 2 1 2.00
2019 92 231 2 CHATO JULIE KENT 2 2 3.90 142 95 70
2019 93 231 2 CHATO JULIE KENT 2 3 3.90 260 90 75
2019 94 231 2 CHATO JULIE KENT 2 4 3.10 124 90 20
2019 95 231 2 CHATO JULIE KENT 2 5 3.60 132 80 70
2019 96 231 2 CHATO JULIE KENT 2 6 3.85 103 60 65
2019 97 231 2 CHATO JULIE KENT 2 7 3.65 104 85 65
2019 98 231 2 CHATO JULIE KENT 2 8 3.70 123 60 80
2019 99 231 2 CHATO JULIE KENT 2 9 3.35 139 70 90
2019 100 141 1 CHULUCANAS CHULUCANAS KENT 2 1 3.85 260 90 75
2019 101 141 1 CHULUCANAS CHULUCANAS KENT 2 2 3.55 215 90 35
2019 102 141 1 CHULUCANAS CHULUCANAS KENT 2 3 3.80 103 90 75
2019 103 141 1 CHULUCANAS CHULUCANAS KENT 2 4 3.45 181 90 65
2019 104 141 1 CHULUCANAS CHULUCANAS KENT 2 5 4.15 360 80 80
2019 105 141 1 CHULUCANAS CHULUCANAS KENT 2 6 3.45 98 60 20
2019 106 141 1 CHULUCANAS CHULUCANAS KENT 2 7 3.40 229 95 70
2019 107 141 1 CHULUCANAS CHULUCANAS KENT 2 8 3.30 159 90 70
2019 108 141 1 CHULUCANAS CHULUCANAS KENT 2 9 3.40 211 90 60
2019 109 221 7 CHATO CHATO KENT 2 1 3.35 88 70 40
2019 110 221 7 CHATO CHATO KENT 2 2 3.15 164 80 50
2019 111 221 7 CHATO CHATO KENT 2 3 3.30 127 70 50
2019 112 221 7 CHATO CHATO KENT 2 4 4.25 130 90 30
2019 113 221 7 CHATO CHATO KENT 2 5 2.80 218 70 65
2019 114 221 7 CHATO CHATO KENT 2 6 4.35 280 90 50
2019 115 221 7 CHATO CHATO KENT 2 7 3.95 125 80 50
2019 116 221 7 CHATO CHATO KENT 2 8 4.35 150 80 40
2019 117 221 7 CHATO CHATO KENT 2 9 3.50 102 80 50
2019 118 111 6 CHULUCANAS IRWIN KENT 2 1
2019 119 111 6 CHULUCANAS IRWIN KENT 2 2 3.30
2019 120 111 6 CHULUCANAS IRWIN KENT 2 3 3.20 95 90 60
2019 121 111 6 CHULUCANAS IRWIN KENT 2 4 3.60 134 70 60
2019 122 111 6 CHULUCANAS IRWIN KENT 2 5 3.70 205 70 30
2019 123 111 6 CHULUCANAS IRWIN KENT 2 6 4.10 152 90 30
2019 124 111 6 CHULUCANAS IRWIN KENT 2 7 3.90 125 90 60
2019 125 111 6 CHULUCANAS IRWIN KENT 2 8 4.20 219 70 80
2019 126 111 6 CHULUCANAS IRWIN KENT 2 9 3.50 241 80 80
2019 127 241 3 CHULUCANAS CHATO KENT 2 1 3.30 172 60
2019 128 241 3 CHULUCANAS CHATO KENT 2 2 3.50 220 85 75
2019 129 241 3 CHULUCANAS CHATO KENT 2 3 3.85 152 90 70
2019 130 241 3 CHULUCANAS CHATO KENT 2 4 3.95 130 90 50
2019 131 241 3 CHULUCANAS CHATO KENT 2 5 1.90 3 60 90
2019 132 241 3 CHULUCANAS CHATO KENT 2 6 1.85 216 80 70
2019 133 241 3 CHULUCANAS CHATO KENT 2 7 4.30 181 90 90
2019 134 241 3 CHULUCANAS CHATO KENT 2 8 3.45 176 80 70
2019 135 241 3 CHULUCANAS CHATO KENT 2 9 2.75 75 90 10
2019 136 131 5 CHULUCANAS JULIE KENT 2 1 2.75 120 90 70
2019 137 131 5 CHULUCANAS JULIE KENT 2 2 3.50 151 50 70
2019 138 131 5 CHULUCANAS JULIE KENT 2 3 3.40 135 80 80
2019 139 131 5 CHULUCANAS JULIE KENT 2 4 3.65 250 70 80
2019 140 131 5 CHULUCANAS JULIE KENT 2 5 3.10 188 70 75
2019 141 131 5 CHULUCANAS JULIE KENT 2 6
2019 142 131 5 CHULUCANAS JULIE KENT 2 7 2.90 121 90 40
2019 143 131 5 CHULUCANAS JULIE KENT 2 8 3.45 138 70 80
2019 144 131 5 CHULUCANAS JULIE KENT 2 9 2.90 145 90 70
2019 145 121 8 CHATO CHULUCANAS KENT 3 1 3.20 271 50 50
2019 146 121 8 CHATO CHULUCANAS KENT 3 2 3.40 221 40 60
2019 147 121 8 CHATO CHULUCANAS KENT 3 3 3.60 208 80 70
2019 148 121 8 CHATO CHULUCANAS KENT 3 4 3.30 168 50 45
2019 149 121 8 CHATO CHULUCANAS KENT 3 5 3.00 42 7 15
2019 150 121 8 CHATO CHULUCANAS KENT 3 6 2.85 150 50 65
2019 151 121 8 CHATO CHULUCANAS KENT 3 7 3.65 214 70 75
2019 152 121 8 CHATO CHULUCANAS KENT 3 8 3.65 346 70 80
2019 153 121 8 CHATO CHULUCANAS KENT 3 9 3.00 190 50 60
2019 154 211 4 CHATO IRWIN KENT 3 1 4.20 400 90 80
2019 155 211 4 CHATO IRWIN KENT 3 2 3.30 183 60
2019 156 211 4 CHATO IRWIN KENT 3 3 4.30 263 90 10
2019 157 211 4 CHATO IRWIN KENT 3 4 3.30 250 55 70
2019 158 211 4 CHATO IRWIN KENT 3 5
2019 159 211 4 CHATO IRWIN KENT 3 6 3.65 262 90 65
2019 160 211 4 CHATO IRWIN KENT 3 7 3.65 382 90 30
2019 161 211 4 CHATO IRWIN KENT 3 8 4.00 475 90 30
2019 162 211 4 CHATO IRWIN KENT 3 9 4.05 393 90 60
2019 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 4.15 470 90 70
2019 164 141 1 CHULUCANAS CHULUCANAS KENT 3 2 4.20 287 80 80
2019 165 141 1 CHULUCANAS CHULUCANAS KENT 3 3 4.15 414 80 80
2019 166 141 1 CHULUCANAS CHULUCANAS KENT 3 4 3.16 497 75 80
2019 167 141 1 CHULUCANAS CHULUCANAS KENT 3 5 3.85 279 90 75
2019 168 141 1 CHULUCANAS CHULUCANAS KENT 3 6 3.40 179 50 75
2019 169 141 1 CHULUCANAS CHULUCANAS KENT 3 7 3.40 208 95 20
2019 170 141 1 CHULUCANAS CHULUCANAS KENT 3 8 3.20 256 90 70
2019 171 141 1 CHULUCANAS CHULUCANAS KENT 3 9 2.80 104 80 75
2019 172 231 2 CHATO JULIE KENT 3 1 4.45 539 90 30
2019 173 231 2 CHATO JULIE KENT 3 2 3.70 402 90 20
2019 174 231 2 CHATO JULIE KENT 3 3 3.85 310 90 20
2019 175 231 2 CHATO JULIE KENT 3 4 3.30 103 90 50
2019 176 231 2 CHATO JULIE KENT 3 5 3.40 50 60 50
2019 177 231 2 CHATO JULIE KENT 3 6 3.40 200 80 10
2019 178 231 2 CHATO JULIE KENT 3 7 3.95 324 90 75
2019 179 231 2 CHATO JULIE KENT 3 8 4.05 354 90 75
2019 180 231 2 CHATO JULIE KENT 3 9 3.25 135 70 70
2019 181 111 6 CHULUCANAS IRWIN KENT 3 1 2.80 130 80 70
2019 182 111 6 CHULUCANAS IRWIN KENT 3 2 3.00 140 80 40
2019 183 111 6 CHULUCANAS IRWIN KENT 3 3 3.65 284 80 80
2019 184 111 6 CHULUCANAS IRWIN KENT 3 4 3.65 261 90 80
2019 185 111 6 CHULUCANAS IRWIN KENT 3 5 3.75 220 95 20
2019 186 111 6 CHULUCANAS IRWIN KENT 3 6 3.15 175 70 80
2019 187 111 6 CHULUCANAS IRWIN KENT 3 7 3.60 310 95 70
2019 188 111 6 CHULUCANAS IRWIN KENT 3 8 3.60 440 90 70
2019 189 111 6 CHULUCANAS IRWIN KENT 3 9 4.20 415 90 70
2019 190 221 7 CHATO CHATO KENT 3 1 3.70 360 50 70
2019 191 221 7 CHATO CHATO KENT 3 2 4.70 230 90 15
2019 192 221 7 CHATO CHATO KENT 3 3 4.10 350 90 20
2019 193 221 7 CHATO CHATO KENT 3 4 4.00 423 90 70
2019 194 221 7 CHATO CHATO KENT 3 5 3.50 304 90 60
2019 195 221 7 CHATO CHATO KENT 3 6
2019 196 221 7 CHATO CHATO KENT 3 7 4.00 410 90 80
2019 197 221 7 CHATO CHATO KENT 3 8 3.90 380 90 80
2019 198 221 7 CHATO CHATO KENT 3 9 4.05 340 90 50
2019 199 131 5 CHULUCANAS JULIE KENT 3 1
2019 200 131 5 CHULUCANAS JULIE KENT 3 2 3.70 268 80 80
2019 201 131 5 CHULUCANAS JULIE KENT 3 3 3.40 190 85 80
2019 202 131 5 CHULUCANAS JULIE KENT 3 4 3.75 214 80 80
2019 203 131 5 CHULUCANAS JULIE KENT 3 5 3.70 380 90 80
2019 204 131 5 CHULUCANAS JULIE KENT 3 6 214
2019 205 131 5 CHULUCANAS JULIE KENT 3 7 3.60 300 95 80
2019 206 131 5 CHULUCANAS JULIE KENT 3 8 3.40 325 90 60
2019 207 131 5 CHULUCANAS JULIE KENT 3 9 3.70 350 90 70
2019 208 241 3 CHULUCANAS CHATO KENT 3 1 3.50 180 80 75
2019 209 241 3 CHULUCANAS CHATO KENT 3 2 4.40 295 90 25
2019 210 241 3 CHULUCANAS CHATO KENT 3 3 3.60 232 70 50
2019 211 241 3 CHULUCANAS CHATO KENT 3 4 4.05 250 70 80
2019 212 241 3 CHULUCANAS CHATO KENT 3 5 4.10 340 90 80
2019 213 241 3 CHULUCANAS CHATO KENT 3 6 4.05 382 80 80
2019 214 241 3 CHULUCANAS CHATO KENT 3 7 3.70 379 90 70
2019 215 241 3 CHULUCANAS CHATO KENT 3 8 3.50 120 80 70
2019 216 241 3 CHULUCANAS CHATO KENT 3 9 3.65 260 90 70

fru %>% kable(caption = "Evaluation of mango fruit quality", align = 'c')
Evaluation of mango fruit quality
year n treat n_treat stock edge scion block n_plant sample n_fruits weigth long diameter_1 diameter_2 diameter_average
2023 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 1 26 270 86 78 74 76.0
2023 2 141 1 CHULUCANAS CHULUCANAS KENT 1 1 2 26 520 111 90 83 86.5
2023 3 141 1 CHULUCANAS CHULUCANAS KENT 1 1 3 26 385 103 86 75 80.5
2023 4 141 1 CHULUCANAS CHULUCANAS KENT 1 1 4 26 230 86 70 69 69.5
2023 5 141 1 CHULUCANAS CHULUCANAS KENT 1 1 5 26 285 88 78 71 74.5
2023 6 141 1 CHULUCANAS CHULUCANAS KENT 1 2 1 35 600 116 96 87 91.5
2023 7 141 1 CHULUCANAS CHULUCANAS KENT 1 2 2 35 457 101 88 88 88.0
2023 8 141 1 CHULUCANAS CHULUCANAS KENT 1 2 3 35 520 115 88 81 84.5
2023 9 141 1 CHULUCANAS CHULUCANAS KENT 1 2 4 35 665 126 103 95 99.0
2023 10 141 1 CHULUCANAS CHULUCANAS KENT 1 2 5 35 305 91 78 80 79.0
2023 11 231 2 CHATO JULIE KENT 1 1 1 118 422 100 91 83 87.0
2023 12 231 2 CHATO JULIE KENT 1 1 2 118 377 102 84 76 80.0
2023 13 231 2 CHATO JULIE KENT 1 1 3 118 300 91 84 72 78.0
2023 14 231 2 CHATO JULIE KENT 1 1 4 118 455 106 88 82 85.0
2023 15 231 2 CHATO JULIE KENT 1 1 5 118 485 108 91 83 87.0
2023 16 231 2 CHATO JULIE KENT 1 2 1 102 420 104 87 83 85.0
2023 17 231 2 CHATO JULIE KENT 1 2 2 102 395 99 86 80 83.0
2023 18 231 2 CHATO JULIE KENT 1 2 3 102 335 98 87 70 78.5
2023 19 231 2 CHATO JULIE KENT 1 2 4 102 610 118 99 92 95.5
2023 20 231 2 CHATO JULIE KENT 1 2 5 102 530 109 98 90 94.0
2023 21 241 3 CHULUCANAS CHATO KENT 1 1 1 86 485 105 90 83 86.5
2023 22 241 3 CHULUCANAS CHATO KENT 1 1 2 86 460 103 89 80 84.5
2023 23 241 3 CHULUCANAS CHATO KENT 1 1 3 86 576 111 101 86 93.5
2023 24 241 3 CHULUCANAS CHATO KENT 1 1 4 86 610 114 97 91 94.0
2023 25 241 3 CHULUCANAS CHATO KENT 1 1 5 86 505 111 92 85 88.5
2023 26 241 3 CHULUCANAS CHATO KENT 1 2 1 105 475 109 88 84 86.0
2023 27 241 3 CHULUCANAS CHATO KENT 1 2 2 105 350 95 83 77 80.0
2023 28 241 3 CHULUCANAS CHATO KENT 1 2 3 105 345 96 86 74 80.0
2023 29 241 3 CHULUCANAS CHATO KENT 1 2 4 105 475 101 95 86 90.5
2023 30 241 3 CHULUCANAS CHATO KENT 1 2 5 105 490 111 97 83 90.0
2023 31 211 4 CHATO IRWIN KENT 1 1 1 16 560 104 99 90 94.5
2023 32 211 4 CHATO IRWIN KENT 1 1 2 16 381 96 83 82 82.5
2023 33 211 4 CHATO IRWIN KENT 1 1 3 16 556 108 95 89 92.0
2023 34 211 4 CHATO IRWIN KENT 1 1 4 16 432 100 89 82 85.5
2023 35 211 4 CHATO IRWIN KENT 1 1 5 16 475 103 89 81 85.0
2023 36 211 4 CHATO IRWIN KENT 1 2 1 97 600 112 99 89 94.0
2023 37 211 4 CHATO IRWIN KENT 1 2 2 97 485 108 93 85 89.0
2023 38 211 4 CHATO IRWIN KENT 1 2 3 97 500 108 91 88 89.5
2023 39 211 4 CHATO IRWIN KENT 1 2 4 97 550 111 96 86 91.0
2023 40 211 4 CHATO IRWIN KENT 1 2 5 97 695 119 106 94 100.0
2023 41 131 5 CHULUCANAS JULIE KENT 1 1 1 94 367 100 82 80 81.0
2023 42 131 5 CHULUCANAS JULIE KENT 1 1 2 94 448 110 89 78 83.5
2023 43 131 5 CHULUCANAS JULIE KENT 1 1 3 94 400 104 87 77 82.0
2023 44 131 5 CHULUCANAS JULIE KENT 1 1 4 94 409 106 86 78 82.0
2023 45 131 5 CHULUCANAS JULIE KENT 1 1 5 94 402 102 90 80 85.0
2023 46 131 5 CHULUCANAS JULIE KENT 1 2 1 118 460 107 86 80 83.0
2023 47 131 5 CHULUCANAS JULIE KENT 1 2 2 118 450 107 94 81 87.5
2023 48 131 5 CHULUCANAS JULIE KENT 1 2 3 118 416 108 82 77 79.5
2023 49 131 5 CHULUCANAS JULIE KENT 1 2 4 118 500 107 97 85 91.0
2023 50 131 5 CHULUCANAS JULIE KENT 1 2 5 118 465 112 93 82 87.5
2023 51 111 6 CHULUCANAS IRWIN KENT 1 1 1 91 605 120 94 91 92.5
2023 52 111 6 CHULUCANAS IRWIN KENT 1 1 2 91 585 122 93 86 89.5
2023 53 111 6 CHULUCANAS IRWIN KENT 1 1 3 91 415 106 85 79 82.0
2023 54 111 6 CHULUCANAS IRWIN KENT 1 1 4 91 460 103 89 81 85.0
2023 55 111 6 CHULUCANAS IRWIN KENT 1 1 5 91 455 105 86 82 84.0
2023 56 111 6 CHULUCANAS IRWIN KENT 1 2 1 71 375 96 88 79 83.5
2023 57 111 6 CHULUCANAS IRWIN KENT 1 2 2 71 455 108 94 84 89.0
2023 58 111 6 CHULUCANAS IRWIN KENT 1 2 3 71 410 101 84 77 80.5
2023 59 111 6 CHULUCANAS IRWIN KENT 1 2 4 71 460 110 89 79 84.0
2023 60 111 6 CHULUCANAS IRWIN KENT 1 2 5 71 385 105 82 78 80.0
2023 61 221 7 CHATO CHATO KENT 1 1 1 13 440 100 90 81 85.5
2023 62 221 7 CHATO CHATO KENT 1 1 2 13 450 104 90 84 87.0
2023 63 221 7 CHATO CHATO KENT 1 1 3 13 540 110 91 88 89.5
2023 64 221 7 CHATO CHATO KENT 1 1 4 13 368 97 82 81 81.5
2023 65 221 7 CHATO CHATO KENT 1 1 5 13 341 93 85 78 81.5
2023 66 221 7 CHATO CHATO KENT 1 2 1 31 500 117 92 79 85.5
2023 67 221 7 CHATO CHATO KENT 1 2 2 31 305 96 81 71 76.0
2023 68 221 7 CHATO CHATO KENT 1 2 3 31 425 103 89 82 85.5
2023 69 221 7 CHATO CHATO KENT 1 2 4 31 360 101 82 78 80.0
2023 70 221 7 CHATO CHATO KENT 1 2 5 31 380 101 84 78 81.0
2023 71 121 8 CHATO CHULUCANAS KENT 1 1 1 29 340 95 80 77 78.5
2023 72 121 8 CHATO CHULUCANAS KENT 1 1 2 29 390 100 82 77 79.5
2023 73 121 8 CHATO CHULUCANAS KENT 1 1 3 29 480 110 92 81 86.5
2023 74 121 8 CHATO CHULUCANAS KENT 1 1 4 29 440 102 87 80 83.5
2023 75 121 8 CHATO CHULUCANAS KENT 1 1 5 29 450 104 90 83 86.5
2023 76 121 8 CHATO CHULUCANAS KENT 1 2 1 62 371 100 83 78 80.5
2023 77 121 8 CHATO CHULUCANAS KENT 1 2 2 62 381 95 89 81 85.0
2023 78 121 8 CHATO CHULUCANAS KENT 1 2 3 62 380 102 87 79 83.0
2023 79 121 8 CHATO CHULUCANAS KENT 1 2 4 62 452 105 91 84 87.5
2023 80 121 8 CHATO CHULUCANAS KENT 1 2 5 62 455 104 90 84 87.0
2023 81 141 1 CHULUCANAS CHULUCANAS KENT 2 1 1 51 385 98 90 79 84.5
2023 82 141 1 CHULUCANAS CHULUCANAS KENT 2 1 2 51 610 119 96 87 91.5
2023 83 141 1 CHULUCANAS CHULUCANAS KENT 2 1 3 51 640 120 100 86 93.0
2023 84 141 1 CHULUCANAS CHULUCANAS KENT 2 1 4 51 430 103 92 80 86.0
2023 85 141 1 CHULUCANAS CHULUCANAS KENT 2 1 5 51 500 107 95 87 91.0
2023 86 141 1 CHULUCANAS CHULUCANAS KENT 2 2 1 38 630 118 101 91 96.0
2023 87 141 1 CHULUCANAS CHULUCANAS KENT 2 2 2 38 512 107 97 86 91.5
2023 88 141 1 CHULUCANAS CHULUCANAS KENT 2 2 3 38 652 126 102 83 92.5
2023 89 141 1 CHULUCANAS CHULUCANAS KENT 2 2 4 38 510 108 93 88 90.5
2023 90 141 1 CHULUCANAS CHULUCANAS KENT 2 2 5 38 495 110 95 84 89.5
2023 91 231 2 CHATO JULIE KENT 2 1 1 57 441 102 93 82 87.5
2023 92 231 2 CHATO JULIE KENT 2 1 2 57 471 105 88 83 85.5
2023 93 231 2 CHATO JULIE KENT 2 1 3 57 645 120 100 95 97.5
2023 94 231 2 CHATO JULIE KENT 2 1 4 57 525 114 90 85 87.5
2023 95 231 2 CHATO JULIE KENT 2 1 5 57 360 101 89 72 80.5
2023 96 231 2 CHATO JULIE KENT 2 2 1 116 555 115 99 87 93.0
2023 97 231 2 CHATO JULIE KENT 2 2 2 116 495 116 91 80 85.5
2023 98 231 2 CHATO JULIE KENT 2 2 3 116 512 114 93 84 88.5
2023 99 231 2 CHATO JULIE KENT 2 2 4 116 605 119 95 85 90.0
2023 100 231 2 CHATO JULIE KENT 2 2 5 116 520 109 99 87 93.0
2023 101 241 3 CHULUCANAS CHATO KENT 2 1 1 91 425 102 91 82 86.5
2023 102 241 3 CHULUCANAS CHATO KENT 2 1 2 91 600 118 101 88 94.5
2023 103 241 3 CHULUCANAS CHATO KENT 2 1 3 91 765 119 107 94 100.5
2023 104 241 3 CHULUCANAS CHATO KENT 2 1 4 91 426 108 87 76 81.5
2023 105 241 3 CHULUCANAS CHATO KENT 2 1 5 91 475 106 97 84 90.5
2023 106 241 3 CHULUCANAS CHATO KENT 2 2 1 94 545 109 95 86 90.5
2023 107 241 3 CHULUCANAS CHATO KENT 2 2 2 94 371 100 85 78 81.5
2023 108 241 3 CHULUCANAS CHATO KENT 2 2 3 94 470 102 89 85 87.0
2023 109 241 3 CHULUCANAS CHATO KENT 2 2 4 94 445 105 94 83 88.5
2023 110 241 3 CHULUCANAS CHATO KENT 2 2 5 94 575 115 96 85 90.5
2023 111 211 4 CHATO IRWIN KENT 2 1 1 123 489 110 95 83 89.0
2023 112 211 4 CHATO IRWIN KENT 2 1 2 123 370 99 88 78 83.0
2023 113 211 4 CHATO IRWIN KENT 2 1 3 123 472 108 93 82 87.5
2023 114 211 4 CHATO IRWIN KENT 2 1 4 123 460 108 91 78 84.5
2023 115 211 4 CHATO IRWIN KENT 2 1 5 123 391 103 89 77 83.0
2023 116 211 4 CHATO IRWIN KENT 2 2 1 89 450 106 88 80 84.0
2023 117 211 4 CHATO IRWIN KENT 2 2 2 89 535 107 98 89 93.5
2023 118 211 4 CHATO IRWIN KENT 2 2 3 89 410 104 87 75 81.0
2023 119 211 4 CHATO IRWIN KENT 2 2 4 89 490 106 95 83 89.0
2023 120 211 4 CHATO IRWIN KENT 2 2 5 89 376 98 88 74 81.0
2023 121 131 5 CHULUCANAS JULIE KENT 2 1 1 109 385 98 89 79 84.0
2023 122 131 5 CHULUCANAS JULIE KENT 2 1 2 109 400 100 85 80 82.5
2023 123 131 5 CHULUCANAS JULIE KENT 2 1 3 109 635 120 100 88 94.0
2023 124 131 5 CHULUCANAS JULIE KENT 2 1 4 109 616 123 93 81 87.0
2023 125 131 5 CHULUCANAS JULIE KENT 2 1 5 109 445 108 89 78 83.5
2023 126 131 5 CHULUCANAS JULIE KENT 2 2 1 63 585 120 96 81 88.5
2023 127 131 5 CHULUCANAS JULIE KENT 2 2 2 63 388 99 88 81 84.5
2023 128 131 5 CHULUCANAS JULIE KENT 2 2 3 63 717 129 106 87 96.5
2023 129 131 5 CHULUCANAS JULIE KENT 2 2 4 63 478 107 95 83 89.0
2023 130 131 5 CHULUCANAS JULIE KENT 2 2 5 63 445 108 89 78 83.5
2023 131 111 6 CHULUCANAS IRWIN KENT 2 1 1 126 460 101 94 83 88.5
2023 132 111 6 CHULUCANAS IRWIN KENT 2 1 2 126 440 99 89 86 87.5
2023 133 111 6 CHULUCANAS IRWIN KENT 2 1 3 126 535 112 96 86 91.0
2023 134 111 6 CHULUCANAS IRWIN KENT 2 1 4 126 560 118 95 87 91.0
2023 135 111 6 CHULUCANAS IRWIN KENT 2 1 5 126 420 106 84 78 81.0
2023 136 111 6 CHULUCANAS IRWIN KENT 2 2 1 56 380 100 84 77 80.5
2023 137 111 6 CHULUCANAS IRWIN KENT 2 2 2 56 368 98 85 75 80.0
2023 138 111 6 CHULUCANAS IRWIN KENT 2 2 3 56 500 114 92 84 88.0
2023 139 111 6 CHULUCANAS IRWIN KENT 2 2 4 56 310 92 80 75 77.5
2023 140 111 6 CHULUCANAS IRWIN KENT 2 2 5 56 400 100 84 79 81.5
2023 141 221 7 CHATO CHATO KENT 2 1 1 103 405 100 89 79 84.0
2023 142 221 7 CHATO CHATO KENT 2 1 2 103 460 107 90 83 86.5
2023 143 221 7 CHATO CHATO KENT 2 1 3 103 470 107 90 81 85.5
2023 144 221 7 CHATO CHATO KENT 2 1 4 103 410 105 88 74 81.0
2023 145 221 7 CHATO CHATO KENT 2 1 5 103 610 115 103 92 97.5
2023 146 221 7 CHATO CHATO KENT 2 2 1 124 470 111 92 79 85.5
2023 147 221 7 CHATO CHATO KENT 2 2 2 124 435 109 86 79 82.5
2023 148 221 7 CHATO CHATO KENT 2 2 3 124 445 107 85 80 82.5
2023 149 221 7 CHATO CHATO KENT 2 2 4 124 430 104 91 79 85.0
2023 150 221 7 CHATO CHATO KENT 2 2 5 124 639 115 99 90 94.5
2023 151 121 8 CHATO CHULUCANAS KENT 2 1 1 95 615 119 98 89 93.5
2023 152 121 8 CHATO CHULUCANAS KENT 2 1 2 95 430 103 92 82 87.0
2023 153 121 8 CHATO CHULUCANAS KENT 2 1 3 95 460 104 89 84 86.5
2023 154 121 8 CHATO CHULUCANAS KENT 2 1 4 95 391 94 92 82 87.0
2023 155 121 8 CHATO CHULUCANAS KENT 2 1 5 95 410 100 93 80 86.5
2023 156 121 8 CHATO CHULUCANAS KENT 2 2 1 79 489 109 87 83 85.0
2023 157 121 8 CHATO CHULUCANAS KENT 2 2 2 79 569 113 98 89 93.5
2023 158 121 8 CHATO CHULUCANAS KENT 2 2 3 79 566 116 95 84 89.5
2023 159 121 8 CHATO CHULUCANAS KENT 2 2 4 79 475 106 96 84 90.0
2023 160 121 8 CHATO CHULUCANAS KENT 2 2 5 79 490 110 91 82 86.5
2023 161 141 1 CHULUCANAS CHULUCANAS KENT 3 1 1 85 361 95 86 78 82.0
2023 162 141 1 CHULUCANAS CHULUCANAS KENT 3 1 2 85 460 105 93 81 87.0
2023 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 3 85 465 103 94 82 88.0
2023 164 141 1 CHULUCANAS CHULUCANAS KENT 3 1 4 85 450 108 84 80 82.0
2023 165 141 1 CHULUCANAS CHULUCANAS KENT 3 1 5 85 552 111 97 84 90.5
2023 166 141 1 CHULUCANAS CHULUCANAS KENT 3 2 1 113 351 96 84 77 80.5
2023 167 141 1 CHULUCANAS CHULUCANAS KENT 3 2 2 113 584 117 97 84 90.5
2023 168 141 1 CHULUCANAS CHULUCANAS KENT 3 2 3 113 440 109 89 77 83.0
2023 169 141 1 CHULUCANAS CHULUCANAS KENT 3 2 4 113 420 103 90 80 85.0
2023 170 141 1 CHULUCANAS CHULUCANAS KENT 3 2 5 113 361 100 86 72 79.0
2023 171 231 2 CHATO JULIE KENT 3 1 1 57 645 120 102 89 95.5
2023 172 231 2 CHATO JULIE KENT 3 1 2 57 440 105 83 79 81.0
2023 173 231 2 CHATO JULIE KENT 3 1 3 57 300 97 81 70 75.5
2023 174 231 2 CHATO JULIE KENT 3 1 4 57 495 113 96 81 88.5
2023 175 231 2 CHATO JULIE KENT 3 1 5 57 460 111 88 78 83.0
2023 176 231 2 CHATO JULIE KENT 3 2 1 53 360 102 89 72 80.5
2023 177 231 2 CHATO JULIE KENT 3 2 2 53 444 108 86 82 84.0
2023 178 231 2 CHATO JULIE KENT 3 2 3 53 489 112 91 83 87.0
2023 179 231 2 CHATO JULIE KENT 3 2 4 53 488 111 93 82 87.5
2023 180 231 2 CHATO JULIE KENT 3 2 5 53 400 101 87 79 83.0
2023 181 241 3 CHULUCANAS CHATO KENT 3 1 1 87 655 123 101 91 96.0
2023 182 241 3 CHULUCANAS CHATO KENT 3 1 2 87 392 103 88 78 83.0
2023 183 241 3 CHULUCANAS CHATO KENT 3 1 3 87 316 96 83 73 78.0
2023 184 241 3 CHULUCANAS CHATO KENT 3 1 4 87 420 101 86 83 84.5
2023 185 241 3 CHULUCANAS CHATO KENT 3 1 5 87 410 103 87 79 83.0
2023 186 241 3 CHULUCANAS CHATO KENT 3 2 1 64 490 108 88 82 85.0
2023 187 241 3 CHULUCANAS CHATO KENT 3 2 2 64 400 95 89 82 85.5
2023 188 241 3 CHULUCANAS CHATO KENT 3 2 3 64 575 112 96 89 92.5
2023 189 241 3 CHULUCANAS CHATO KENT 3 2 4 64 613 112 98 91 94.5
2023 190 241 3 CHULUCANAS CHATO KENT 3 2 5 64 410 103 88 80 84.0
2023 191 211 4 CHATO IRWIN KENT 3 1 1 92 530 113 99 88 93.5
2023 192 211 4 CHATO IRWIN KENT 3 1 2 92 391 101 90 77 83.5
2023 193 211 4 CHATO IRWIN KENT 3 1 3 92 418 102 89 77 83.0
2023 194 211 4 CHATO IRWIN KENT 3 1 4 92 450 100 87 83 85.0
2023 195 211 4 CHATO IRWIN KENT 3 1 5 92 550 112 97 85 91.0
2023 196 211 4 CHATO IRWIN KENT 3 2 1 96 510 112 93 80 86.5
2023 197 211 4 CHATO IRWIN KENT 3 2 2 96 700 124 102 90 96.0
2023 198 211 4 CHATO IRWIN KENT 3 2 3 96 470 104 89 87 88.0
2023 199 211 4 CHATO IRWIN KENT 3 2 4 96 350 95 82 77 79.5
2023 200 211 4 CHATO IRWIN KENT 3 2 5 96 415 101 89 80 84.5
2023 201 131 5 CHULUCANAS JULIE KENT 3 1 1 64 450 108 91 78 84.5
2023 202 131 5 CHULUCANAS JULIE KENT 3 1 2 64 420 109 90 75 82.5
2023 203 131 5 CHULUCANAS JULIE KENT 3 1 3 64 640 120 104 90 97.0
2023 204 131 5 CHULUCANAS JULIE KENT 3 1 4 64 520 111 91 83 87.0
2023 205 131 5 CHULUCANAS JULIE KENT 3 1 5 64 625 126 95 86 90.5
2023 206 131 5 CHULUCANAS JULIE KENT 3 2 1 162 490 110 89 82 85.5
2023 207 131 5 CHULUCANAS JULIE KENT 3 2 2 162 515 107 92 84 88.0
2023 208 131 5 CHULUCANAS JULIE KENT 3 2 3 162 390 103 85 75 80.0
2023 209 131 5 CHULUCANAS JULIE KENT 3 2 4 162 465 105 89 80 84.5
2023 210 131 5 CHULUCANAS JULIE KENT 3 2 5 162 615 118 100 89 94.5
2023 211 111 6 CHULUCANAS IRWIN KENT 3 1 1 128 465 105 90 80 85.0
2023 212 111 6 CHULUCANAS IRWIN KENT 3 1 2 128 535 116 93 85 89.0
2023 213 111 6 CHULUCANAS IRWIN KENT 3 1 3 128 375 100 85 76 80.5
2023 214 111 6 CHULUCANAS IRWIN KENT 3 1 4 128 410 103 86 77 81.5
2023 215 111 6 CHULUCANAS IRWIN KENT 3 1 5 128 435 109 87 81 84.0
2023 216 111 6 CHULUCANAS IRWIN KENT 3 2 1 93 452 108 88 81 84.5
2023 217 111 6 CHULUCANAS IRWIN KENT 3 2 2 93 540 117 92 84 88.0
2023 218 111 6 CHULUCANAS IRWIN KENT 3 2 3 93 426 105 85 79 82.0
2023 219 111 6 CHULUCANAS IRWIN KENT 3 2 4 93 366 106 84 75 79.5
2023 220 111 6 CHULUCANAS IRWIN KENT 3 2 5 93 435 108 93 78 85.5
2023 221 221 7 CHATO CHATO KENT 3 1 1 113 615 120 101 85 93.0
2023 222 221 7 CHATO CHATO KENT 3 1 2 113 505 113 90 80 85.0
2023 223 221 7 CHATO CHATO KENT 3 1 3 113 430 107 86 78 82.0
2023 224 221 7 CHATO CHATO KENT 3 1 4 113 475 106 92 83 87.5
2023 225 221 7 CHATO CHATO KENT 3 1 5 113 325 93 81 72 76.5
2023 226 221 7 CHATO CHATO KENT 3 2 1 99 470 106 89 82 85.5
2023 227 221 7 CHATO CHATO KENT 3 2 2 99 480 109 96 80 88.0
2023 228 221 7 CHATO CHATO KENT 3 2 3 99 525 109 97 85 91.0
2023 229 221 7 CHATO CHATO KENT 3 2 4 99 452 113 94 77 85.5
2023 230 221 7 CHATO CHATO KENT 3 2 5 99 416 101 89 82 85.5
2023 231 121 8 CHATO CHULUCANAS KENT 3 1 1 103 470 108 90 81 85.5
2023 232 121 8 CHATO CHULUCANAS KENT 3 1 2 103 410 105 88 76 82.0
2023 233 121 8 CHATO CHULUCANAS KENT 3 1 3 103 450 111 89 81 85.0
2023 234 121 8 CHATO CHULUCANAS KENT 3 1 4 103 620 119 100 86 93.0
2023 235 121 8 CHATO CHULUCANAS KENT 3 1 5 103
2023 236 121 8 CHATO CHULUCANAS KENT 3 2 1 108 471 111 90 80 85.0
2023 237 121 8 CHATO CHULUCANAS KENT 3 2 2 108 475 110 93 81 87.0
2023 238 121 8 CHATO CHULUCANAS KENT 3 2 3 108 525 114 91 81 86.0
2023 239 121 8 CHATO CHULUCANAS KENT 3 2 4 108 426 100 86 82 84.0
2023 240 121 8 CHATO CHULUCANAS KENT 3 2 5 108 527 113 88 85 86.5

3 Data summary

Summary of the number of data points recorded for each treatment and evaluated variable.

sm <- rdt %>% 
  group_by(year, treat) %>% 
  summarise(across(height:sproud, ~ sum(!is.na(.))))

sm
## # A tibble: 24 × 6
## # Groups:   year [3]
##    year  treat height n_fruits flowering sproud
##    <fct> <fct>  <int>    <int>     <int>  <int>
##  1 2017  111        0       26        26      0
##  2 2017  121        0       26        26      0
##  3 2017  131        0       25        25      0
##  4 2017  141        0       26        26      0
##  5 2017  211        0       26        26      0
##  6 2017  221        0       26        26      0
##  7 2017  231        0       26        26      0
##  8 2017  241        0       27        27      0
##  9 2018  111        0        9        22      0
## 10 2018  121        0        9        23      0
## # ℹ 14 more rows

sm <- fru %>% 
  group_by(year, treat) %>% 
  summarise(across(weigth:diameter_average, ~ sum(!is.na(.))))

sm
## # A tibble: 8 × 7
## # Groups:   year [1]
##   year  treat weigth  long diameter_1 diameter_2 diameter_average
##   <fct> <fct>  <int> <int>      <int>      <int>            <int>
## 1 2023  111       30    30         30         30               30
## 2 2023  121       29    29         29         29               29
## 3 2023  131       30    30         30         30               30
## 4 2023  141       30    30         30         30               30
## 5 2023  211       30    30         30         30               30
## 6 2023  221       30    30         30         30               30
## 7 2023  231       30    30         30         30               30
## 8 2023  241       30    30         30         30               30

4 Meteorological data

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 2

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8, linetype = "longdash") + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8, linetype = "dotted") +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8, linetype = "twodash") +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 50)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "3 month", date_labels = "%b-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.jpg", units = "cm"
         , width = 25, height = 15)

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.eps", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/Figure_2.jpg")

5 Objetives

Evaluate the effect of the rootstock-interstock interaction on the agronomic traits and fruit biometrics of the mango crop in the San Lorenzo Valley.

5.1 Specific Objective 1

Determine the effect of the rootstock-interstock interaction on the agronomic characteristics of mango.

5.1.1 Plant height

trait <- "height"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block      stock  edge height      resi   res_MAD   rawp.BHStud
## 87    523     2      CHATO JULIE   2.00 -1.643199 -3.984232 0.00006769863
## 126   563     2 CHULUCANAS CHATO   1.90 -1.731293 -4.197830 0.00002694848
## 127   564     2 CHULUCANAS CHATO   1.85 -1.781293 -4.319064 0.00001566924
##              adjp       bholm out_flag
## 87  0.00006769863 0.013810521  OUTLIER
## 126 0.00002694848 0.005524438  OUTLIER
## 127 0.00001566924 0.003227863  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: height
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## block        2  3.140 1.57012  9.1832 0.000155 ***
## stock        1  1.273 1.27268  7.4435 0.006954 ** 
## edge         3  1.994 0.66450  3.8865 0.009977 ** 
## stock:edge   3  2.301 0.76689  4.4853 0.004549 ** 
## Residuals  193 32.999 0.17098                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ stock|edge) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()
stock edge emmean SE df lower.CL upper.CL group
CHATO CHATO 3.741984 0.0811085 193 3.582011 3.901957 Aa
CHATO CHULUCANAS 3.662963 0.0795773 193 3.506010 3.819916 Aa
CHATO IRWIN 3.915061 0.0811085 193 3.755088 4.075034 Aa
CHATO JULIE 3.860337 0.0827154 193 3.697194 4.023479 Aa
CHULUCANAS CHATO 3.925048 0.0827647 193 3.761808 4.088287 Aa
CHULUCANAS CHULUCANAS 3.497320 0.0811085 193 3.337347 3.657293 Ab
CHULUCANAS IRWIN 3.649114 0.0844563 193 3.482538 3.815690 Bab
CHULUCANAS JULIE 3.467553 0.0844563 193 3.300977 3.634129 Bb

p1a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Plant height (m)"
           , glab = "Interstock"
           , ylimits = c(0, 6, 2)
           , 
           )

p1a

5.1.2 Sproud

trait <- "sproud"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block      stock       edge sproud      resi   res_MAD  rawp.BHStud
## 8     441     1 CHULUCANAS CHULUCANAS      0 -63.26923 -4.267451 0.0000197719
## 126   567     2 CHULUCANAS      CHATO     10 -58.26923 -3.930206 0.0000848732
##             adjp       bholm out_flag
## 8   0.0000197719 0.003974153  OUTLIER
## 126 0.0000848732 0.016974639  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: sproud
##             Df Sum Sq Mean Sq F value     Pr(>F)    
## block        2    362   180.9  0.5152    0.59820    
## stock        1   6579  6578.9 18.7416 0.00002425 ***
## edge         3   2054   684.7  1.9506    0.12292    
## stock:edge   3   2713   904.2  2.5758    0.05521 .  
## Residuals  189  66345   351.0                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ stock|edge) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()
stock edge emmean SE df lower.CL upper.CL group
CHATO CHATO 54.81356 3.747943 189 47.42039 62.20674 Ba
CHATO CHULUCANAS 65.17927 3.675132 189 57.92972 72.42882 Aa
CHATO IRWIN 52.08471 3.750192 189 44.68710 59.48232 Aa
CHATO JULIE 51.91004 3.675132 189 44.66049 59.15959 Ba
CHULUCANAS CHATO 70.57288 3.750234 189 63.17519 77.97057 Aa
CHULUCANAS CHULUCANAS 65.94241 3.750188 189 58.54481 73.34001 Aa
CHULUCANAS IRWIN 61.21227 3.910119 189 53.49919 68.92535 Aa
CHULUCANAS JULIE 72.15745 3.826839 189 64.60864 79.70625 Aa

p1b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Sproud ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           )

p1b 

5.1.3 Number of fruits

trait <- "n_fruits"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block year stock  edge treat n_fruits     resi  res_MAD   rawp.BHStud
## 442   604     3 2019 CHATO JULIE   231      539 287.2486 4.008689 0.00006105685
##              adjp      bholm out_flag
## 442 0.00006105685 0.02955151  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: n_fruits
##             Df  Sum Sq Mean Sq F value          Pr(>F)    
## block        2  261207  130604 21.6839 0.0000000009805 ***
## year         2  126248   63124 10.4804 0.0000352211768 ***
## stock        1    9326    9326  1.5483          0.2140    
## edge         3   22131    7377  1.2248          0.3001    
## stock:edge   3   15548    5183  0.8605          0.4615    
## Residuals  471 2836870    6023                            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock|year) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()
year edge stock emmean SE df lower.CL upper.CL group
2017 CHATO CHATO 162.5132 10.74838 471 141.3925 183.6339 Ba
2017 CHATO CHULUCANAS 170.3860 10.59352 471 149.5696 191.2024 Ba
2017 CHULUCANAS CHATO 171.7745 10.74778 471 150.6550 192.8940 Ba
2017 CHULUCANAS CHULUCANAS 198.8729 10.74778 471 177.7533 219.9924 Ba
2017 IRWIN CHATO 178.3001 10.74838 471 157.1794 199.4208 Ba
2017 IRWIN CHULUCANAS 174.7851 10.92618 471 153.3150 196.2552 Ba
2017 JULIE CHATO 173.0122 10.86339 471 151.6655 194.3589 Ba
2017 JULIE CHULUCANAS 176.5933 10.91078 471 155.1535 198.0331 Ba
2018 CHATO CHATO 197.2613 13.04649 471 171.6248 222.8978 Aa
2018 CHATO CHULUCANAS 205.1341 12.95537 471 179.6767 230.5916 Aa
2018 CHULUCANAS CHATO 206.5226 13.04648 471 180.8861 232.1591 Aa
2018 CHULUCANAS CHULUCANAS 233.6210 13.04648 471 207.9845 259.2575 Aa
2018 IRWIN CHATO 213.0482 13.04649 471 187.4117 238.6847 Aa
2018 IRWIN CHULUCANAS 209.5332 13.19501 471 183.6048 235.4616 Aa
2018 JULIE CHATO 207.7604 13.14195 471 181.9362 233.5845 Aa
2018 JULIE CHULUCANAS 211.3414 13.14337 471 185.5145 237.1683 Aa
2019 CHATO CHATO 194.2890 10.75243 471 173.1603 215.4177 Aa
2019 CHATO CHULUCANAS 202.1618 10.59847 471 181.3357 222.9880 Aa
2019 CHULUCANAS CHATO 203.5503 10.75303 471 182.4205 224.6802 Aa
2019 CHULUCANAS CHULUCANAS 230.6487 10.75303 471 209.5188 251.7785 Aa
2019 IRWIN CHATO 210.0759 10.75243 471 188.9472 231.2046 Aa
2019 IRWIN CHULUCANAS 206.5609 11.07069 471 184.8068 228.3150 Aa
2019 JULIE CHATO 204.7881 10.95974 471 183.2520 226.3241 Aa
2019 JULIE CHULUCANAS 208.3691 10.91480 471 186.9214 229.8169 Aa

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Fruits number"
           , glab = "Interstock"
           , ylimits = c(0, 320, 60)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1c 

5.1.4 Flowering

trait <- "flowering"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block year      stock       edge treat flowering      resi   res_MAD
## 28     31     1 2017      CHATO      IRWIN   211         5 -68.74456 -6.212394
## 32     35     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 33     36     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 64     67     1 2017      CHATO      CHATO   221        20 -56.92226 -5.144022
## 65     68     1 2017      CHATO      CHATO   221        30 -46.92226 -4.240330
## 67     70     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 69     72     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 127   131     2 2017 CHULUCANAS      CHATO   241        25 -58.44225 -5.281382
## 166   171     3 2017 CHULUCANAS CHULUCANAS   141        20 -58.28192 -5.266893
## 213   225     1 2018 CHULUCANAS CHULUCANAS   141        20 -57.68630 -5.213068
## 232   258     1 2018 CHULUCANAS      JULIE   131        10 -56.04249 -5.064518
## 243   273     1 2018 CHULUCANAS      IRWIN   111        15 -58.16121 -5.255986
## 250   284     1 2018      CHATO      CHATO   221        90  47.03144  4.250196
## 251   288     1 2018      CHATO      CHATO   221        97  54.03144  4.882781
## 254   292     2 2018      CHATO      IRWIN   211         5 -63.87075 -5.771952
## 289   328     2 2018      CHATO      CHATO   221         3 -43.83305 -3.961160
## 292   331     2 2018      CHATO      CHATO   221         0 -46.83305 -4.232268
## 298   338     2 2018 CHULUCANAS      IRWIN   111        10 -67.02571 -6.057063
## 299   339     2 2018 CHULUCANAS      IRWIN   111         5 -72.02571 -6.508909
## 311   352     2 2018 CHULUCANAS      JULIE   131        20 -49.90698 -4.510056
## 333   376     3 2018      CHATO      IRWIN   211        20 -44.36027 -4.008804
## 342   385     3 2018 CHULUCANAS CHULUCANAS   141         0 -77.04031 -6.962075
## 346   389     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 347   390     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 349   392     3 2018      CHATO      JULIE   231         2 -45.63760 -4.124236
## 375   421     3 2018 CHULUCANAS      JULIE   131        10 -55.39650 -5.006140
## 376   422     3 2018 CHULUCANAS      JULIE   131        20 -45.39650 -4.102448
## 454   505     2 2019      CHATO      IRWIN   211        30 -48.76953 -4.407266
## 526   581     3 2019      CHATO CHULUCANAS   121         7 -70.54614 -6.375202
##              rawp.BHStud                 adjp             bholm out_flag
## 28  0.000000000521833021 0.000000000521833021 0.000000305794150  OUTLIER
## 32  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 33  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 64  0.000000268917649482 0.000000268917649482 0.000155165483751  OUTLIER
## 65  0.000022319169655338 0.000022319169655338 0.012632650024921  OUTLIER
## 67  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 69  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 127 0.000000128212855666 0.000000128212855666 0.000074491669142  OUTLIER
## 166 0.000000138751758749 0.000000138751758749 0.000080476020075  OUTLIER
## 213 0.000000185743196379 0.000000185743196379 0.000107359567507  OUTLIER
## 232 0.000000409435427784 0.000000409435427784 0.000235834806404  OUTLIER
## 243 0.000000147233846226 0.000000147233846226 0.000085248396965  OUTLIER
## 250 0.000021358339519217 0.000021358339519217 0.012110178507396  OUTLIER
## 251 0.000001046000612570 0.000001046000612570 0.000600404351615  OUTLIER
## 254 0.000000007835861071 0.000000007835861071 0.000004576142866  OUTLIER
## 289 0.000074586480278027 0.000074586480278027 0.041843015435973  OUTLIER
## 292 0.000023134658141855 0.000023134658141855 0.013071081850148  OUTLIER
## 298 0.000000001386292858 0.000000001386292858 0.000000810981322  OUTLIER
## 299 0.000000000075698336 0.000000000075698336 0.000000044510622  OUTLIER
## 311 0.000006481038199890 0.000006481038199890 0.003713634888537  OUTLIER
## 333 0.000061026989556012 0.000061026989556012 0.034297168130479  OUTLIER
## 342 0.000000000003352874 0.000000000003352874 0.000000001974843  OUTLIER
## 346 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 347 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 349 0.000037196739853362 0.000037196739853362 0.020978961277296  OUTLIER
## 375 0.000000555322711371 0.000000555322711371 0.000319310559038  OUTLIER
## 376 0.000040880207851313 0.000040880207851313 0.023015557020289  OUTLIER
## 454 0.000010468378508710 0.000010468378508710 0.005987912506982  OUTLIER
## 526 0.000000000182721394 0.000000000182721394 0.000000107257458  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: flowering
##             Df Sum Sq Mean Sq F value            Pr(>F)    
## block        2   1978   988.9  3.3541           0.03566 *  
## year         2  16473  8236.6 27.9371 0.000000000002796 ***
## stock        1    804   803.6  2.7257           0.09932 .  
## edge         3   7276  2425.3  8.2263 0.000023116374039 ***
## stock:edge   3   1580   526.5  1.7859           0.14874    
## Residuals  548 161564   294.8                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock|year) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()
year edge stock emmean SE df lower.CL upper.CL group
2017 CHATO CHATO 76.99530 2.345782 548 72.38748 81.60313 Ab
2017 CHATO CHULUCANAS 77.04841 2.208166 548 72.71090 81.38591 Ab
2017 CHULUCANAS CHATO 87.25102 2.226577 548 82.87735 91.62469 Aa
2017 CHULUCANAS CHULUCANAS 85.96947 2.254128 548 81.54168 90.39726 Aa
2017 IRWIN CHATO 80.99148 2.311773 548 76.45046 85.53250 Aab
2017 IRWIN CHULUCANAS 86.68903 2.284768 548 82.20105 91.17700 Aa
2017 JULIE CHATO 78.20890 2.263368 548 73.76296 82.65484 Ab
2017 JULIE CHULUCANAS 84.54702 2.315901 548 79.99789 89.09615 Aa
2018 CHATO CHATO 64.90407 2.425731 548 60.13920 69.66894 Bb
2018 CHATO CHULUCANAS 64.95718 2.269647 548 60.49890 69.41545 Bb
2018 CHULUCANAS CHATO 75.15979 2.289284 548 70.66294 79.65663 Ba
2018 CHULUCANAS CHULUCANAS 73.87824 2.329225 548 69.30293 78.45354 Ba
2018 IRWIN CHATO 68.90025 2.375187 548 64.23467 73.56584 Bab
2018 IRWIN CHULUCANAS 74.59780 2.389683 548 69.90374 79.29186 Ba
2018 JULIE CHATO 66.11767 2.367724 548 61.46675 70.76860 Bb
2018 JULIE CHULUCANAS 72.45579 2.429118 548 67.68427 77.22731 Ba
2019 CHATO CHATO 77.01196 2.305001 548 72.48424 81.53968 Ab
2019 CHATO CHULUCANAS 77.06506 2.198633 548 72.74628 81.38384 Ab
2019 CHULUCANAS CHATO 87.26768 2.233767 548 82.87988 91.65547 Aa
2019 CHULUCANAS CHULUCANAS 85.98612 2.243058 548 81.58008 90.39217 Aa
2019 IRWIN CHATO 81.00814 2.291841 548 76.50627 85.51001 Aab
2019 IRWIN CHULUCANAS 86.70568 2.312242 548 82.16374 91.24763 Aa
2019 JULIE CHATO 78.22556 2.271401 548 73.76384 82.68728 Ab
2019 JULIE CHULUCANAS 84.56367 2.325199 548 79.99628 89.13107 Aa

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Flowering ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1d

5.1.5 Figure 3

Univariate analysis of the variables that determine the agronomic characteristics of mango.

legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1a + theme(legend.position="none")
           , p1b + theme(legend.position="none")
           , p1c + theme(legend.position="none")
           , p1d + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            , rel_heights = c(1, 2)
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.jpg"
         , units = "cm"
         , width = 24
         , height = 16
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.eps"
         , units = "cm"
         , width = 24
         , height = 16
         )

knitr::include_graphics("submission/Figure_3.jpg")

5.1.6 Multivariate

Principal Component Analysis (PCA) of agronomic traits in the mango crop based on the use of rootstock-interstock combinations.

mv <- rdt %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Height = height
         , Fruits = n_fruits
         , Flowering = flowering
         , Sproud = sproud)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4
## Variance               2.49   0.93   0.37   0.21
## % of var.             62.27  23.26   9.22   5.26
## Cumulative % of var.  62.27  85.52  94.74 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  2.28 | -1.99 19.84  0.76 | -0.72  7.04  0.10 | -0.43
## 2                     |  1.67 |  1.05  5.51  0.39 | -0.61  4.97  0.13 |  1.15
## 3                     |  2.18 | -1.90 18.19  0.76 |  0.89 10.71  0.17 |  0.36
## 4                     |  1.74 | -1.44 10.38  0.68 |  0.94 11.90  0.29 | -0.19
## 5                     |  1.52 | -0.49  1.23  0.11 | -1.05 14.72  0.47 | -0.41
## 6                     |  2.83 |  2.26 25.69  0.64 |  1.63 35.67  0.33 | -0.51
## 7                     |  1.00 |  0.68  2.34  0.46 | -0.03  0.01  0.00 |  0.67
## 8                     |  2.21 |  1.83 16.82  0.69 | -1.06 14.98  0.23 | -0.63
##                         ctr  cos2  
## 1                      6.36  0.04 |
## 2                     44.91  0.47 |
## 3                      4.40  0.03 |
## 4                      1.21  0.01 |
## 5                      5.78  0.07 |
## 6                      8.90  0.03 |
## 7                     15.00  0.44 |
## 8                     13.43  0.08 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Height                | -0.91 33.26  0.83 |  0.07  0.53  0.00 |  0.25 17.31
## Fruits                |  0.57 13.26  0.33 |  0.78 66.19  0.62 | -0.16  6.81
## Flowering             |  0.86 29.71  0.74 |  0.05  0.23  0.00 |  0.51 69.79
## Sproud                |  0.77 23.77  0.59 | -0.55 33.05  0.31 | -0.15  6.10
##                        cos2  
## Height                 0.06 |
## Fruits                 0.03 |
## Flowering              0.26 |
## Sproud                 0.02 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  2.28 | -1.99  0.76  -1.26 | -0.72  0.10  -0.75 | -0.43
## CHATO-CHULUCANAS      |  1.67 |  1.05  0.39   0.66 | -0.61  0.13  -0.63 |  1.15
## CHATO-IRWIN           |  2.18 | -1.90  0.76  -1.21 |  0.89  0.17   0.93 |  0.36
## CHATO-JULIE           |  1.74 | -1.44  0.68  -0.91 |  0.94  0.29   0.98 | -0.19
## CHULUCANAS-CHATO      |  1.52 | -0.49  0.11  -0.31 | -1.05  0.47  -1.09 | -0.41
## CHULUCANAS-CHULUCANAS |  2.83 |  2.26  0.64   1.43 |  1.63  0.33   1.69 | -0.51
## CHULUCANAS-IRWIN      |  1.00 |  0.68  0.46   0.43 | -0.03  0.00  -0.03 |  0.67
## CHULUCANAS-JULIE      |  2.21 |  1.83  0.69   1.16 | -1.06  0.23  -1.09 | -0.63
##                        cos2 v.test  
## CHATO-CHATO            0.04  -0.71 |
## CHATO-CHULUCANAS       0.47   1.90 |
## CHATO-IRWIN            0.03   0.59 |
## CHATO-JULIE            0.01  -0.31 |
## CHULUCANAS-CHATO       0.07  -0.68 |
## CHULUCANAS-CHULUCANAS  0.03  -0.84 |
## CHULUCANAS-IRWIN       0.44   1.10 |
## CHULUCANAS-JULIE       0.08  -1.04 |

f4a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                , label = "var"
                )

f4b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                ) 

5.1.7 Figure 4

Principal Component Analysis (PCA). Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 50) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 

png("corrplot_output.png", width = 800, height = 800, res = 150)  
corrplot(var$cor, 
         method = "number",
         tl.col = "black", 
         tl.srt = 45)
dev.off()
## png 
##   2

img_corrplot <- ggdraw() + draw_image("corrplot_output.png")

pt4 <- img_corrplot

# Construcción del grid

fila1 <- plot_grid(f4a, f4b, ncol = 2, labels = "auto", rel_widths = c(1, 1.5))

columna_c <- plot_grid(pt1, pt2, pt3, ncol = 1, labels = "c")

fila2 <- plot_grid(columna_c, pt4, ncol = 2, labels = c("", "d"), rel_widths = c(1, 1))

grid_final <- plot_grid(fila1, fila2, ncol = 1, rel_heights = c(1, 1.2))

grid_final



ggsave2(plot = grid_final, "submission/Figure_4.jpg", height = 30, width = 28, units = "cm")

ggsave2(plot = grid_final, "submission/Figure_4.eps", height = 30, width = 28, units = "cm")

knitr::include_graphics("submission/Figure_4.jpg")

5.2 Specific Objective 2

Determine the effect of the rootstock-interstock interaction on the fruit biometrics of mango.

5.2.1 Fruit Weigth

trait <- "weigth"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       block       stock       edge        weigth      resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: weigth
##             Df  Sum Sq Mean Sq F value  Pr(>F)  
## block        2   73498   36749  4.6207 0.01078 *
## stock        1    1816    1816  0.2284 0.63320  
## edge         3    4318    1439  0.1810 0.90923  
## stock:edge   3   37870   12623  1.5872 0.19323  
## Residuals  229 1821257    7953                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
1 IRWIN CHATO 482.0333 16.28198 229 449.9517 514.1150 a
2 JULIE CHATO 465.9667 16.28198 229 433.8850 498.0483 a
4 CHULUCANAS CHATO 462.3661 16.56272 229 429.7313 495.0009 a
3 CHATO CHATO 452.5333 16.28198 229 420.4517 484.6150 a
7 CHATO CHULUCANAS 484.9667 16.28198 229 452.8850 517.0483 a
6 JULIE CHULUCANAS 484.7000 16.28198 229 452.6184 516.7816 a
8 CHULUCANAS CHULUCANAS 468.1667 16.28198 229 436.0850 500.2483 a
5 IRWIN CHULUCANAS 447.2333 16.28198 229 415.1517 479.3150 a

p2a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit Weigth (g)"
           , glab = "Interstock"
           , ylimits = c(0, 600, 100)
           , 
           )

p2a

5.2.2 Fruit length

trait <- "long"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       block       stock       edge        long        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: long
##             Df  Sum Sq Mean Sq F value   Pr(>F)   
## block        2   681.4  340.70  5.9312 0.003081 **
## stock        1    31.2   31.16  0.5424 0.462192   
## edge         3   270.1   90.02  1.5672 0.198107   
## stock:edge   3    39.5   13.15  0.2289 0.876184   
## Residuals  229 13154.4   57.44                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
1 JULIE CHATO 107.6667 1.383746 229 104.9402 110.3932 a
3 CHULUCANAS CHATO 106.3048 1.407606 229 103.5313 109.0783 a
2 IRWIN CHATO 106.0667 1.383746 229 103.3402 108.7932 a
4 CHATO CHATO 105.9667 1.383746 229 103.2402 108.6932 a
6 JULIE CHULUCANAS 109.7333 1.383746 229 107.0068 112.4598 a
7 CHATO CHULUCANAS 106.5333 1.383746 229 103.8068 109.2598 a
5 IRWIN CHULUCANAS 106.4333 1.383746 229 103.7068 109.1598 a
8 CHULUCANAS CHULUCANAS 106.2000 1.383746 229 103.4735 108.9265 a

p2b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit length (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 120, 20)
           , 
           )

p2b

5.2.3 Fruit diameter

trait <- "diameter_average"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index            block            stock            edge            
##  [5] diameter_average resi             res_MAD          rawp.BHStud     
##  [9] adjp             bholm            out_flag        
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: diameter_average
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## block        2  248.1 124.033  5.0489 0.007149 **
## stock        1    1.2   1.178  0.0480 0.826864   
## edge         3    6.1   2.027  0.0825 0.969504   
## stock:edge   3  234.3  78.109  3.1796 0.024804 * 
## Residuals  229 5625.6  24.566                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
1 IRWIN CHATO 87.63333 0.9049151 229 85.85031 89.41636 a
2 JULIE CHATO 86.23333 0.9049151 229 84.45031 88.01636 a
4 CHULUCANAS CHATO 86.07080 0.9205182 229 84.25703 87.88457 a
3 CHATO CHATO 85.23333 0.9049151 229 83.45031 87.01636 a
7 CHATO CHULUCANAS 87.70000 0.9049151 229 85.91698 89.48302 a
6 JULIE CHULUCANAS 86.30000 0.9049151 229 84.51698 88.08302 a
8 CHULUCANAS CHULUCANAS 86.08333 0.9049151 229 84.30031 87.86636 a
5 IRWIN CHULUCANAS 84.53333 0.9049151 229 82.75031 86.31636 a

p2c <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit diameter (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           , 
           )

p2c

5.2.4 Table 2

Descriptive statistics of the variables that determine the fruit biometrics of mango.

sts <- Summarize(weigth  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1a <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit Weigth (g)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(long  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1b <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit length (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(diameter_average ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1c <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit diameter (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

tb1 <- bind_rows(tb1a, tb1b, tb1c)

tb1 %>% kable(align = 'c')
Variable Rootstock Interstock mean sd min max Sig
Fruit Weigth (g) CHATO CHATO 452.53 79.92 305.0 639.0 a
Fruit Weigth (g) CHATO CHULUCANAS 462.34 69.82 340.0 620.0 a
Fruit Weigth (g) CHATO IRWIN 482.03 87.30 350.0 700.0 a
Fruit Weigth (g) CHATO JULIE 465.97 92.07 300.0 645.0 a
Fruit Weigth (g) CHULUCANAS CHATO 484.97 101.98 316.0 765.0 a
Fruit Weigth (g) CHULUCANAS CHULUCANAS 468.17 118.73 230.0 665.0 a
Fruit Weigth (g) CHULUCANAS IRWIN 447.23 70.09 310.0 605.0 a
Fruit Weigth (g) CHULUCANAS JULIE 484.70 93.46 367.0 717.0 a
Fruit length (mm) CHATO CHATO 105.97 6.78 93.0 120.0 a
Fruit length (mm) CHATO CHULUCANAS 106.28 6.80 94.0 119.0 a
Fruit length (mm) CHATO IRWIN 106.07 6.47 95.0 124.0 a
Fruit length (mm) CHATO JULIE 107.67 7.54 91.0 120.0 a
Fruit length (mm) CHULUCANAS CHATO 106.53 7.21 95.0 123.0 a
Fruit length (mm) CHULUCANAS CHULUCANAS 106.20 10.87 86.0 126.0 a
Fruit length (mm) CHULUCANAS IRWIN 106.43 7.28 92.0 122.0 a
Fruit length (mm) CHULUCANAS JULIE 109.73 8.03 98.0 129.0 a
Fruit diameter (mm) CHATO CHATO 85.23 4.72 76.0 97.5 a
Fruit diameter (mm) CHATO CHULUCANAS 86.09 3.61 78.5 93.5 a
Fruit diameter (mm) CHATO IRWIN 87.63 5.07 79.5 100.0 a
Fruit diameter (mm) CHATO JULIE 86.23 5.56 75.5 97.5 a
Fruit diameter (mm) CHULUCANAS CHATO 87.70 5.40 78.0 100.5 a
Fruit diameter (mm) CHULUCANAS CHULUCANAS 86.08 6.61 69.5 99.0 a
Fruit diameter (mm) CHULUCANAS IRWIN 84.53 4.03 77.5 92.5 a
Fruit diameter (mm) CHULUCANAS JULIE 86.30 4.68 79.5 97.0 a

tb1 %>%
  write_sheet(ss = gs, sheet = "tb1")

5.2.5 Multivariate

Principal Component Analysis (PCA) of fruit biometrics in the mango crop based on the use of rootstock-interstock combinations.

mv <- fru %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
  dplyr::select(!c(diameter_1, diameter_2, n_fruits)) %>%
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Weight = weigth
         , Length = long
         , Diameter = diameter_average)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3
## Variance               2.01   0.97   0.02
## % of var.             66.97  32.23   0.79
## Cumulative % of var.  66.97  99.21 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  1.71 | -1.70 17.91  0.98 | -0.22  0.60  0.02 | -0.01
## 2                     |  0.68 | -0.56  1.99  0.69 | -0.37  1.76  0.29 |  0.09
## 3                     |  1.85 |  1.38 11.78  0.56 | -1.23 19.60  0.44 |  0.00
## 4                     |  0.70 |  0.09  0.05  0.02 |  0.61  4.83  0.76 |  0.33
## 5                     |  1.93 |  1.70 17.92  0.78 | -0.91 10.79  0.22 |  0.00
## 6                     |  0.57 | -0.29  0.51  0.25 | -0.44  2.46  0.58 | -0.23
## 7                     |  2.33 | -2.29 32.54  0.96 |  0.45  2.60  0.04 | -0.06
## 8                     |  2.69 |  1.67 17.30  0.38 |  2.11 57.36  0.61 | -0.12
##                         ctr  cos2  
## 1                      0.03  0.00 |
## 2                      4.74  0.02 |
## 3                      0.00  0.00 |
## 4                     57.67  0.22 |
## 5                      0.00  0.00 |
## 6                     28.15  0.16 |
## 7                      2.18  0.00 |
## 8                      7.23  0.00 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Weight                |  0.99 49.14  0.99 | -0.03  0.07  0.00 | -0.11 50.79
## Length                |  0.46 10.55  0.21 |  0.89 81.29  0.79 |  0.04  8.16
## Diameter              |  0.90 40.32  0.81 | -0.42 18.64  0.18 |  0.10 41.05
##                        cos2  
## Weight                 0.01 |
## Length                 0.00 |
## Diameter               0.01 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  1.71 | -1.70  0.98  -1.20 | -0.22  0.02  -0.22 | -0.01
## CHATO-CHULUCANAS      |  0.68 | -0.56  0.69  -0.40 | -0.37  0.29  -0.38 |  0.09
## CHATO-IRWIN           |  1.85 |  1.38  0.56   0.97 | -1.23  0.44  -1.25 |  0.00
## CHATO-JULIE           |  0.70 |  0.09  0.02   0.07 |  0.61  0.76   0.62 |  0.33
## CHULUCANAS-CHATO      |  1.93 |  1.70  0.78   1.20 | -0.91  0.22  -0.93 |  0.00
## CHULUCANAS-CHULUCANAS |  0.57 | -0.29  0.25  -0.20 | -0.44  0.58  -0.44 | -0.23
## CHULUCANAS-IRWIN      |  2.33 | -2.29  0.96  -1.61 |  0.45  0.04   0.46 | -0.06
## CHULUCANAS-JULIE      |  2.69 |  1.67  0.38   1.18 |  2.11  0.61   2.14 | -0.12
##                        cos2 v.test  
## CHATO-CHATO            0.00  -0.05 |
## CHATO-CHULUCANAS       0.02   0.62 |
## CHATO-IRWIN            0.00  -0.02 |
## CHATO-JULIE            0.22   2.15 |
## CHULUCANAS-CHATO       0.00  -0.02 |
## CHULUCANAS-CHULUCANAS  0.16  -1.50 |
## CHULUCANAS-IRWIN       0.00  -0.42 |
## CHULUCANAS-JULIE       0.00  -0.76 |

f5a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                )

f5b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                ) 

5.2.6 Figure 5

Principal Component Analysis (PCA). Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="white") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 60) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 100) + 
  labs(title = "Dim 2 - variables contribution") 

png("corrplot_output.png", width = 800, height = 800, res = 150)  
corrplot(var$cor, 
         method = "number",
         tl.col = "black", 
         tl.srt = 45)
dev.off()
## png 
##   2

img_corrplot <- ggdraw() + draw_image("corrplot_output.png")

pt4 <- img_corrplot

# Construcción del grid

fila1 <- plot_grid(f5a, f5b, ncol = 2, labels = "auto", rel_widths = c(1, 1.2))

columna_c <- plot_grid(pt1, pt2, pt3, ncol = 1, labels = "c")

fila2 <- plot_grid(columna_c, pt4, ncol = 2, labels = c("", "d"), rel_widths = c(1, 1))

grid_final <- plot_grid(fila1, fila2, ncol = 1, rel_heights = c(1, 1.2))

grid_final



ggsave2(plot = grid_final, "submission/Figure_5.jpg", height = 30, width = 28, units = "cm")

ggsave2(plot = grid_final, "submission/Figure_5.eps", height = 30, width = 28, units = "cm")

knitr::include_graphics("submission/Figure_5.jpg")